diff --git a/.github/workflows/deploy.yaml b/.github/workflows/deploy.yaml new file mode 100644 index 00000000..29bfc575 --- /dev/null +++ b/.github/workflows/deploy.yaml @@ -0,0 +1,14 @@ +name: Deploy to GitHub Pages + +permissions: + contents: write + pages: write + +on: + push: + branches: [ "main", "master" ] + workflow_dispatch: +jobs: + deploy: + runs-on: ubuntu-latest + steps: [uses: fastai/workflows/quarto-ghp@master] diff --git a/.github/workflows/test-image.yaml b/.github/workflows/test-image.yaml new file mode 100644 index 00000000..e55c35a9 --- /dev/null +++ b/.github/workflows/test-image.yaml @@ -0,0 +1,18 @@ +name: Python pytest +on: [workflow_dispatch, pull_request, push] + +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: 3.9 + cache: "pip" + cache-dependency-path: settings.ini + - name: Run pytest + run: | + python -m pip install --upgrade pip + pip install .[dev] + pytest nbs/tests/ \ No newline at end of file diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 00000000..56085923 --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,7 @@ +name: CI +on: [workflow_dispatch, pull_request, push] + +jobs: + test: + runs-on: ubuntu-latest + steps: [uses: fastai/workflows/nbdev-ci@master] diff --git a/.gitignore b/.gitignore index d43a704b..5f112e28 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,21 @@ +_docs/ +_proc/ + +*.bak +.gitattributes +.last_checked +.gitconfig +*.bak +*.log +*~ +~* +_tmp* +tmp* +tags +*.pkg + # Byte-compiled / optimized / DLL files __pycache__/ -.idea/* *.py[cod] *$py.class @@ -9,6 +24,7 @@ __pycache__/ # Distribution / packaging .Python +env/ build/ develop-eggs/ dist/ @@ -24,7 +40,6 @@ wheels/ *.egg-info/ .installed.cfg *.egg -MANIFEST # PyInstaller # Usually these files are written by a python script from a template @@ -46,7 +61,6 @@ nosetests.xml coverage.xml *.cover .hypothesis/ -.pytest_cache/ # Translations *.mo @@ -54,8 +68,6 @@ coverage.xml # Django stuff: *.log -.static_storage/ -.media/ local_settings.py # Flask stuff: @@ -83,14 +95,13 @@ celerybeat-schedule # SageMath parsed files *.sage.py -# Environments +# dotenv .env + +# virtualenv .venv -env/ venv/ ENV/ -env.bak/ -venv.bak/ # Spyder project settings .spyderproject @@ -105,20 +116,36 @@ venv.bak/ # mypy .mypy_cache/ -# Add more below here. # +.vscode +*.swp + +# osx generated files +.DS_Store +.DS_Store? +.Trashes +ehthumbs.db +Thumbs.db +.idea + +# pytest +.pytest_cache + +# tools/trust-doc-nbs +docs_src/.last_checked + +# symlinks to fastai +docs_src/fastai +tools/fastai -# dev files -dev* +# link checker +checklink/cookies.txt -# DS_Store -*.DS_Store -**/.DS_Store +# .gitconfig is now autogenerated +.gitconfig -# font list -fontList.json -fontList.py3k.cache -fontList-v300.json +# Quarto installer +.deb +.pkg -# tex folders -tex.cache/ -.Rproj.user +# Quarto +.quarto diff --git a/.pre-commit-config.yaml.yaml b/.pre-commit-config.yaml.yaml new file mode 100644 index 00000000..44be211a --- /dev/null +++ b/.pre-commit-config.yaml.yaml @@ -0,0 +1,6 @@ +repos: +- repo: https://github.com/fastai/nbdev + rev: 2.2.10 + hooks: + - id: nbdev_clean + - id: nbdev_export \ No newline at end of file diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 101a1873..00000000 --- a/.travis.yml +++ /dev/null @@ -1,28 +0,0 @@ -dist: xenial -language: python - -env: - - PYTHON=3.6 BACKEND=agg - - PYTHON=3.7 BACKEND=agg - - PYTHON=3.8 BACKEND=agg - -before_install: - - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh - - bash miniconda.sh -b -p $HOME/miniconda - - export PATH="$HOME/miniconda/bin:$PATH" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - - conda info -a - - -install: - - conda update conda --yes - - conda config --add channels conda-forge - - conda create -n testenv --yes pip python=$PYTHON matplotlib freetype=2.10 - # - if [ "$PYTHON" = "3.8" ]; then conda create -n testenv --yes --channel conda-forge/label/pre-3.8 pip python=$PYTHON; else conda create -n testenv --yes pip python=$PYTHON matplotlib; fi - - source activate testenv - - pip install .[dev] - - -script: pytest dabest diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md deleted file mode 100644 index 191d1ab4..00000000 --- a/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,76 +0,0 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. - -## Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at joseshowh@gmail.com. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 1350d01b..00000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,23 +0,0 @@ -# Contributing to DABEST-Python - - -## Did you find a bug? -- Ensure the bug was not already reported by searching in [Issues](https://github.com/ACCLAB/DABEST-python/issues). Check that the bug hasn't been addressed in a closed issue. - -- If the bug isn't being addressed, open a new one. Be sure to include a title and clear description, and a [minimally reproducible code sample](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) demonstrating the expected behavior that is not occurring. - - -## Did you write a patch that fixes a bug? -- Open a new GitHub [pull request](https://help.github.com/en/articles/about-pull-requests) (PR for short) with the patch. - -- Create the PR into the development branch, which is indicated by `v{latest version number}-dev`. - -- Clearly state the problem and solution in the PR description. Include the relevant [issue number](https://guides.github.com/features/issues/) if applicable. - - -## Do you intend to add a new feature or change an existing one? -- Suggest your change by opening an issue, and adding an Enhancement tag. -- If the maintainers and the community are in favour, create a fork and start writing code. - - -DABEST is a community tool for estimation statistics and analysis. We look forward to more robust and more elegant data visualizations from you all! diff --git a/LICENSE b/LICENSE index b8fd4d93..3b106e87 100644 --- a/LICENSE +++ b/LICENSE @@ -1,32 +1,201 @@ -The Clear BSD License - -Copyright (c) 2016-2020 Joses W. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2022, fastai + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/MANIFEST.in b/MANIFEST.in index 04f196ac..5c0e7ced 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,5 @@ -include README.md +include settings.ini include LICENSE +include CONTRIBUTING.md +include README.md +recursive-exclude * __pycache__ diff --git a/README.md b/README.md index 615caf15..4033e127 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,41 @@ -# DABEST-Python -[![Travis CI build status](https://travis-ci.org/ACCLAB/DABEST-python.svg?branch=master)](https://travis-ci.org/ACCLAB/DABEST-python) -[![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.6-6666ff.svg)](https://www.anaconda.com/distribution/) -[![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest) -[![Downloads](https://pepy.tech/badge/dabest/month)](https://pepy.tech/project/dabest/month) -[![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4) -[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html) +DABEST-Python +================ + + + +## Recent Version Update + +On 20 March 2023, we officially released **DABEST v2023.02.14 for +Python**. This new version provided the following new features: + +1. **Repeated measures.** Augments the prior function for plotting + (independent) multiple test groups versus a shared control; it can + now do the same for repeated-measures experimental designs. Thus, + together, these two methods can be used to replace both flavors of + the 1-way ANOVA with an estimation analysis. + +2. **Proportional data.** Generates proportional bar plots, + proportional differences, and calculates Cohen’s h. Also enables + plotting Sankey diagrams for paired binary data. This is the + estimation equivalent to a bar chart with Fischer’s exact test. + +3. **The $\Delta\Delta$ plot.** Calculates the delta-delta + ($\Delta\Delta$) for 2 × 2 experimental designs and plots the four + groups with their relevant effect sizes. This design can be used as + a replacement for the 2 × 2 ANOVA. + +4. **Mini-meta.** Calculates and plots a weighted delta ($\Delta$) for + meta-analysis of experimental replicates. Useful for summarizing + data from multiple replicated experiments, for example by different + scientists in the same lab, or the same scientist at different + times. When the observed values are known (and share a common + metric), this makes meta-analysis available as a routinely + accessible tool. ## Contents + + - [About](#about) - [Installation](#installation) - [Usage](#usage) @@ -22,49 +50,64 @@ ## About -DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation. +DABEST is a package for **D**ata **A**nalysis using +**B**ootstrap-Coupled **EST**imation. -[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by *P* values. +[Estimation +statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a +[simple framework](https://thenewstatistics.com/itns/) that avoids the +[pitfalls](https://www.nature.com/articles/nmeth.3288) of significance +testing. It uses familiar statistical concepts: means, mean differences, +and error bars. More importantly, it focuses on the effect size of one’s +experiment/intervention, as opposed to a false dichotomy engendered by +*P* values. An estimation plot has two key features. -1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. +1. It presents all datapoints as a swarmplot, which orders each point + to display the underlying distribution. -2. It presents the effect size as a **bootstrap 95% confidence interval** on a **separate but aligned axes**. +2. It presents the effect size as a **bootstrap 95% confidence + interval** on a **separate but aligned axes**. -![The five kinds of estimation plots](docs/source/_images/showpiece.png?raw=true "The five kinds of estimation plots.") - -DABEST powers [estimationstats.com](https://www.estimationstats.com/), allowing everyone access to high-quality estimation plots. +![The five kinds of estimation +plots](showpiece.png "The five kinds of estimation plots.") +DABEST powers [estimationstats.com](https://www.estimationstats.com/), +allowing everyone access to high-quality estimation plots. ## Installation -This package is tested on Python 3.6, 3.7, and 3.8. -It is highly recommended to download the [Anaconda distribution](https://www.continuum.io/downloads) of Python in order to obtain the dependencies easily. +This package is tested on Python 3.6, 3.7, and 3.8. It is highly +recommended to download the [Anaconda +distribution](https://www.continuum.io/downloads) of Python in order to +obtain the dependencies easily. You can install this package via `pip`. -To install, at the command line run - -```shell + +``` shell pip install --upgrade dabest ``` -You can also [clone](https://help.github.com/articles/cloning-a-repository) this repo locally. + +You can also +[clone](https://help.github.com/articles/cloning-a-repository) this repo +locally. Then, navigate to the cloned repo in the command line and run -```shell +``` shell pip install . ``` - ## Usage -```python3 +``` python3 import pandas as pd import dabest @@ -78,57 +121,102 @@ iris_dabest = dabest.load(data=iris, x="species", y="petal_width", # Produce a Cumming estimation plot. iris_dabest.mean_diff.plot(); ``` -![A Cumming estimation plot of petal width from the iris dataset](https://github.com/ACCLAB/DABEST-python/blob/master/iris.png) -Please refer to the official [tutorial](https://acclab.github.io/DABEST-python-docs/tutorial.html) for more useful code snippets. +![A Cumming estimation plot of petal width from the iris +dataset](iris.png) +Please refer to the official +[tutorial](https://acclab.github.io/DABEST-python-docs/tutorial.html) +for more useful code snippets. ## How to cite **Moving beyond P values: Everyday data analysis with estimation plots** -*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang* - -Nature Methods 2019, 1548-7105. [10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3) +*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam +Claridge-Chang* -[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view PDF](https://rdcu.be/bHhJ4) +Nature Methods 2019, 1548-7105. +[10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3) +[Paywalled publisher +site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view +PDF](https://rdcu.be/bHhJ4) ## Bugs -Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/DABEST-python/issues/new). - +Please report any bugs on the [Github issue +tracker](https://github.com/ACCLAB/DABEST-python/issues/new). ## Contributing -All contributions are welcome; please read the [Guidelines for contributing](https://github.com/ACCLAB/DABEST-python/blob/master/CONTRIBUTING.md) first. +All contributions are welcome; please read the [Guidelines for +contributing](https://github.com/ACCLAB/DABEST-python/blob/master/CONTRIBUTING.md) +first. -We also have a [Code of Conduct](https://github.com/ACCLAB/DABEST-python/blob/master/CODE_OF_CONDUCT.md) to foster an inclusive and productive space. +We also have a [Code of +Conduct](https://github.com/ACCLAB/DABEST-python/blob/master/CODE_OF_CONDUCT.md) +to foster an inclusive and productive space. ### A wish list for new features -Currently, DABEST offers functions to handle data traditionally analyzed with Student’s paired and unpaired t-tests. It also offers plots for multiplexed versions of these, and the estimation counterpart to a 1-way analysis of variance (ANOVA), the shared-control design. While these five functions execute a large fraction of common biomedical data analyses, there remain three others: 2-way data, time-series group data, and proportional data. We aim to add these new functions to both the R and Python libraries. - -● In many experiments, four groups are investigate to isolate an interaction, for example: a genotype × drug effect. Here, wild-type and mutant animals are each subjected to drug or sham treatments; the data are traditionally analysed with a 2×2 ANOVA. We have received requests by email, Twitter, and GitHub to implement an estimation counterpart to the 2-way ANOVA. To do this, we will implement ∆∆ plots, in which the difference of means (∆) of two groups is subtracted from a second two-group ∆. - -● Currently, DABEST can analyse multiple paired data in a single plot, and multiple groups with a common, shared control. However, a common design in biomedical science is to follow the same group of subjects over multiple, successive time points. An estimation plot for this would combine elements of the two other designs, and could be used in place of a repeated-measures ANOVA. - -● We have observed that proportional data are often analyzed in neuroscience and other areas of biomedical research. However, compared to other data types, the charts are frequently impoverished: often, they omit error bars, sample sizes, and even P values—let alone effect sizes. We would like DABEST to feature proportion charts, with error bars and a curve for the distribution of the proportional differences. - -We encourage contributions for the above features. +Currently, DABEST offers functions to handle data traditionally analyzed +with Student’s paired and unpaired t-tests. It also offers plots for +multiplexed versions of these, and the estimation counterpart to a 1-way +analysis of variance (ANOVA), the shared-control design. While these +five functions execute a large fraction of common biomedical data +analyses, there remain three others: 2-way data, time-series group data, +and proportional data. We aim to add these new functions to both the R +and Python libraries. + +- In many experiments, four groups are investigate to isolate an + interaction, for example: a genotype × drug effect. Here, wild-type + and mutant animals are each subjected to drug or sham treatments; the + data are traditionally analysed with a 2×2 ANOVA. We have received + requests by email, Twitter, and GitHub to implement an estimation + counterpart to the 2-way ANOVA. To do this, we will implement + $\Delta\Delta$ plots, in which the difference of means ($\Delta$) of + two groups is subtracted from a second two-group $\Delta$. + **Implemented in v2023.02.14.** + +- Currently, DABEST can analyse multiple paired data in a single plot, + and multiple groups with a common, shared control. However, a common + design in biomedical science is to follow the same group of subjects + over multiple, successive time points. An estimation plot for this + would combine elements of the two other designs, and could be used in + place of a repeated-measures ANOVA. **Implemented in v2023.02.14** + +- We have observed that proportional data are often analyzed in + neuroscience and other areas of biomedical research. However, compared + to other data types, the charts are frequently impoverished: often, + they omit error bars, sample sizes, and even P values—let alone effect + sizes. We would like DABEST to feature proportion charts, with error + bars and a curve for the distribution of the proportional differences. + **Implemented in v2023.02.14** + +We encourage contributions for the above features. ## Acknowledgements -We would like to thank alpha testers from the [Claridge-Chang lab](https://www.claridgechang.net/): [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and Stanislav Ott. - +We would like to thank alpha testers from the [Claridge-Chang +lab](https://www.claridgechang.net/): [Sangyu +Xu](https://github.com/sangyu), [Xianyuan +Zhang](https://github.com/XYZfar), [Farhan +Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and +Stanislav Ott. ## Testing -To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest). - -Run `pytest` in the root directory of the source distribution. This runs the test suite in the folder `dabest/tests`. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected. +To test DABEST, you will need to install +[pytest](https://docs.pytest.org/en/latest). +Run `pytest` in the root directory of the source distribution. This runs +the test suite in the folder `dabest/tests`. The test suite will ensure +that the bootstrapping functions and the plotting functions perform as +expected. ## DABEST in other languages -DABEST is also available in R ([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab ([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)). +DABEST is also available in R +([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab +([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)). diff --git a/dabest/__init__.py b/dabest/__init__.py index fd3b571b..faabafbe 100644 --- a/dabest/__init__.py +++ b/dabest/__init__.py @@ -1,26 +1,5 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com - -""" -**dabest** is a Python package for Data Analysis and Visualization using Bootstrap-Coupled Estimation. - -Estimation statistics is a simple framework that—while avoiding the pitfalls of significance testing—uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to significance testing. - -An estimation plot has two key features. Firstly, it presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. Secondly, an estimation plot presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. - -**dabest** creates estimation plots for mean differences, median differences, standardized effect sizes (Cohen's _d_ and Hedges' _g_), and ordinal effect sizes (Cliff's delta). - -Please cite this work as: -Moving beyond P values: Everyday data analysis with estimation plots -Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang -https://doi.org/10.1101/377978 -""" - - from ._api import load from ._stats_tools import effsize as effsize from ._classes import TwoGroupsEffectSize, PermutationTest -__version__ = "0.3.1" +__version__ = "2023.2.14" diff --git a/dabest/_api.py b/dabest/_api.py index c44986d9..b8af2237 100644 --- a/dabest/_api.py +++ b/dabest/_api.py @@ -1,11 +1,13 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/load.ipynb. +# %% auto 0 +__all__ = ['load'] -def load(data, idx, x=None, y=None, paired=False, id_col=None, - ci=95, resamples=5000, random_seed=12345): +# %% ../nbs/API/load.ipynb 4 +def load(data, idx=None, x=None, y=None, paired=None, id_col=None, + ci=95, resamples=5000, random_seed=12345, proportional=False, + delta2 = False, experiment = None, experiment_label = None, + x1_level = None, mini_meta=False): ''' Loads data in preparation for estimation statistics. @@ -18,10 +20,19 @@ def load(data, idx, x=None, y=None, paired=False, id_col=None, List of column names (if 'x' is not supplied) or of category names (if 'x' is supplied). This can be expressed as a tuple of tuples, with each individual tuple producing its own contrast plot - x : string, default None + x : string or list, default None + Column name(s) of the independent variable. This can be expressed as + a list of 2 elements if and only if 'delta2' is True; otherwise it + can only be a string. y : string, default None Column names for data to be plotted on the x-axis and y-axis. - paired : boolean, default False. + paired : string, default None + The type of the experiment under which the data are obtained. If 'paired' + is None then the data will not be treated as paired data in the subsequent + calculations. If 'paired' is 'baseline', then in each tuple of x, other + groups will be paired up with the first group (as control). If 'paired' is + 'sequential', then in each tuple of x, each group will be paired up with + its previous group (as control). id_col : default None. Required if `paired` is True. ci : integer, default 95 @@ -34,32 +45,35 @@ def load(data, idx, x=None, y=None, paired=False, id_col=None, This integer is used to seed the random number generator during bootstrap resampling, ensuring that the confidence intervals reported are replicable. + proportional : boolean, default False. + An indicator of whether the data is binary or not. When set to True, it + specifies that the data consists of binary data, where the values are + limited to 0 and 1. The code is not suitable for analyzing proportion + data that contains non-numeric values, such as strings like 'yes' and 'no'. + When False or not provided, the algorithm assumes that + the data is continuous and uses a non-proportional representation. + delta2 : boolean, default False + Indicator of delta-delta experiment + experiment : String, default None + The name of the column of the dataframe which contains the label of + experiments + experiment_lab : list, default None + A list of String to specify the order of subplots for delta-delta plots. + This can be expressed as a list of 2 elements if and only if 'delta2' + is True; otherwise it can only be a string. + x1_level : list, default None + A list of String to specify the order of subplots for delta-delta plots. + This can be expressed as a list of 2 elements if and only if 'delta2' + is True; otherwise it can only be a string. + mini_meta : boolean, default False + Indicator of weighted delta calculation. Returns ------- A `Dabest` object. - - Example - -------- - Load libraries. - - >>> import numpy as np - >>> import pandas as pd - >>> import dabest - - Create dummy data for demonstration. - - >>> np.random.seed(88888) - >>> N = 10 - >>> c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N) - >>> t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N) - >>> df = pd.DataFrame({'Control 1' : c1, 'Test 1': t1}) - - Load the data. - - >>> my_data = dabest.load(df, idx=("Control 1", "Test 1")) - ''' from ._classes import Dabest - return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed) + return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label, x1_level, mini_meta) + + diff --git a/dabest/_archive/__init__.py b/dabest/_archive/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/dabest/_archive/_api.py b/dabest/_archive/_api.py deleted file mode 100644 index 5e50acee..00000000 --- a/dabest/_archive/_api.py +++ /dev/null @@ -1,1006 +0,0 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com -from __future__ import division - - -def plot(data, idx, x=None, y=None, ci=95, n_boot=5000, random_seed=12345, - - color_col=None, - paired=False, - effect_size="mean_diff", - raw_marker_size=6, - es_marker_size=9, - - swarm_label=None, - contrast_label=None, - swarm_ylim=None, - contrast_ylim=None, - - plot_context='talk', - font_scale=1., - - custom_palette=None, - float_contrast=True, - show_pairs=True, - show_group_count=True, - group_summaries="mean_sd", - - fig_size=None, - dpi=100, - tick_length=10, - tick_pad=7, - - swarmplot_kwargs=None, - violinplot_kwargs=None, - reflines_kwargs=None, - group_summary_kwargs=None, - legend_kwargs=None, - aesthetic_kwargs=None, - ): - - ''' - Takes a pandas DataFrame and produces an estimation plot: - either a Cummings hub-and-spoke plot or a Gardner-Altman contrast plot. - Paired and unpaired options available. - - Keywords: - --------- - data: pandas DataFrame - - idx: tuple - List of column names (if 'x' is not supplied) or of category names - (if 'x' is supplied). This can be expressed as a tuple of tuples, - with each individual tuple producing its own contrast plot. - - x, y: strings, default None - Column names for data to be plotted on the x-axis and y-axis. - - ci: integer, default 95 - The size of the confidence interval desired (in percentage). - - n_boot: integer, default 5000 - Number of bootstrap iterations to perform during calculation of - confidence intervals. - - random_seed: integer, default 12345 - This integer is used to seed the random number generator during - bootstrap resampling. This ensures that the confidence intervals - reported are replicable. - - color_col: string, default None - Column to be used for colors. - - paired: boolean, default False - Whether or not the data is paired. To elaborate. - - effect_size: ['mean_diff', 'cohens_d', 'hedges_g', 'median_diff', - 'cliffs_delta'], default 'mean_diff'. - - raw_marker_size: float, default 7 - The diameter (in points) of the marker dots plotted in the swarmplot. - - es_marker_size: float, default 9 - The size (in points) of the effect size points on the difference axes. - - swarm_label, contrast_label: strings, default None - Set labels for the y-axis of the swarmplot and the contrast plot, - respectively. If `swarm_label` is not specified, it defaults to - "value", unless a column name was passed to `y`. If `contrast_label` - is not specified, it defaults to the effect size being plotted. - - swarm_ylim, contrast_ylim: tuples, default None - The desired y-limits of the raw data (swarmplot) axes and the - difference axes respectively, as a (lower, higher) tuple. If these - are not specified, they will be autoscaled to sensible values. - - plot_context: default 'talk' - Accepts any of seaborn's plotting contexts: 'notebook', 'paper', - 'talk', and 'poster' to determine the scaling of the plot elements. - Read more about the contexts here: - https://seaborn.pydata.org/generated/seaborn.set_context.html - - font_scale: float, default 1. - The font size will be scaled by this number. - - custom_palette: dict, list, or matplotlib color palette, default None - This keyword accepts a dictionary with {'group':'color'} pairings, - a list of RGB colors, or a specified matplotlib palette. This palette - will be used to color the swarmplot. If no `color_col` is specified, - then each group will be colored in sequence according to the palette. - If `color_col` is specified but this is not, the default palette - used is 'tab10'. - - Please take a look at the seaborn commands `sns.color_palette` - and `sns.cubehelix_palette` to generate a custom palette. Both - these functions generate a list of RGB colors. - https://seaborn.pydata.org/generated/seaborn.color_palette.html - https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html - The named colors of matplotlib can be found here: - https://matplotlib.org/examples/color/named_colors.html - - float_contrast: boolean, default True - Whether or not to display the halfviolin bootstrapped difference - distribution alongside the raw data. - - show_pairs: boolean, default True - If the data is paired, whether or not to show the raw data as a - swarmplot, or as slopegraph, with a line joining each pair of - observations. - - show_group_count: boolean, default True - Whether or not the group count (e.g. 'N=10') will be appended to the - xtick labels. - - group_summaries: ['mean_sd', 'median_quartiles', 'None'], default 'mean_sd' - Plots the summary statistics for each group. If 'mean_sd', then the - mean and standard deviation of each group is plotted as a notched - line beside each group. If 'median_quantiles', then the - median and 25th and 75th percentiles of each group is plotted - instead. If 'None', the summaries are not shown. - - fig_size: tuple, default None - The desired dimensions of the figure as a (length, width) tuple. - The default is (5 * ncols, 7), where `ncols` is the number of - pairwise comparisons being plotted. - - dpi: int, default 100 - The dots per inch of the resulting figure. - - tick_length: int, default 12 - The length of the ticks (in points) for both the swarm and contrast - axes. - - tick_pad: int, default 9 - The distance of the tick label from the tick (in points), for both - the swarm and contrast axes. - - swarmplot_kwargs: dict, default None - Pass any keyword arguments accepted by the seaborn `swarmplot` - command here, as a dict. If None, the following keywords are passed - to sns.swarmplot: {'size':10}. - - violinplot_kwargs: dict, default None - Pass any keyword arguments accepted by the matplotlib ` - pyplot.violinplot` command here, as a dict. If None, the following - keywords are passed to violinplot: {'widths':0.5, 'vert':True, - 'showextrema':False, 'showmedians':False}. - - reflines_kwargs: dict, default None - This will change the appearance of the zero reference lines. Pass - any keyword arguments accepted by the matplotlib Axes `hlines` - command here, as a dict. If None, the following keywords are passed - to Axes.hlines: {'linestyle':'solid', 'linewidth':0.75, 'color':'k'}. - - group_summary_kwargs: dict, default None - Pass any keyword arguments accepted by the matplotlib.lines.Line2D - command here, as a dict. This will change the appearance of the - vertical summary lines for each group, if `group_summaries` is not - 'None'. If None, the following keywords are passed to Line2D: - {'lw': 4, 'color': 'k','alpha': 1, 'zorder': 5}. - - legend_kwargs: dict, default None - Pass any keyword arguments accepted by the matplotlib Axes `legend` - command here, as a dict. If None, the following keywords are passed - to Axes.legend: - {'loc': 'upper left', 'frameon': False, 'bbox_to_anchor': (0.95, 1.), - 'markerscale': 2}. - - aesthetic_kwargs: dict, default None - Pass any keyword arguments accepted by the seaborn `set` command - here, as a dict. - - - Returns: - -------- - matplotlib Figure, and a pandas DataFrame. - - The matplotlib Figure consists of several axes. The odd-numbered axes - are the swarmplot axes. The even-numbered axes are the contrast axes. - Every group in `idx` will have its own pair of axes. You can access each - axes via `figure.axes[i]`. - - - The pandas DataFrame contains the estimation statistics for every - comparison being plotted. The following columns are presented: - stat_summary - The mean difference. - - bca_ci_low - The lower bound of the confidence interval. - - bca_ci_high - The upper bound of the confidence interval. - - ci - The width of the confidence interval, typically 95%. - - pvalue_2samp_ind_ttest - P-value obtained from scipy.stats.ttest_ind. Only produced - if paired is False. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_ind.html - - pvalue_mann_whitney: float - Two-sided p-value obtained from scipy.stats.mannwhitneyu. - Only produced if paired is False. - The Mann-Whitney U-test is a nonparametric unpaired test of - the null hypothesis that x1 and x2 are from the same distribution. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.mannwhitneyu.html - - pvalue_2samp_related_ttest - P-value obtained from scipy.stats.ttest_rel. Only produced - if paired is True. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_rel.html - - pvalue_wilcoxon: float - P-value obtained from scipy.stats.wilcoxon. Only produced - if paired is True. - The Wilcoxons signed-rank test is a nonparametric paired - test of the null hypothesis that the paired samples x1 and - x2 are from the same distribution. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/scipy.stats.wilcoxon.html - ''' - import warnings - # This filters out an innocuous warning when pandas is imported, - # but the version has not been compiled against the newest numpy. - warnings.filterwarnings("ignore", message="numpy.dtype size changed") - # This filters out a "FutureWarning: elementwise comparison failed; - # returning scalar instead, but in the future will perform - # elementwise comparison". Not exactly sure what is causing it.... - warnings.simplefilter(action='ignore', category=FutureWarning) - - import matplotlib as mpl - import matplotlib.pyplot as plt - import matplotlib.ticker as tk - import matplotlib.lines as mlines - # from mpl_toolkits.axes_grid1 import make_axes_locatable - plt.rcParams['svg.fonttype'] = 'none' - - import numpy as np - from scipy.stats import ttest_ind, ttest_rel, wilcoxon, mannwhitneyu - - import seaborn as sns - import pandas as pd - - from .stats_tools.confint_2group_diff import difference_ci - - from .plot_tools import halfviolin, align_yaxis, rotate_ticks - from .plot_tools import gapped_lines, get_swarm_spans - # from .bootstrap_tools import bootstrap, jackknife_indexes, bca - from .misc_tools import merge_two_dicts, unpack_and_add - - - # Make a copy of the data, so we don't make alterations to it. - data_in = data.copy() - data_in.reset_index(inplace=True) - - - # Determine the kind of estimation plot we need to produce. - if all([isinstance(i, str) for i in idx]): - plottype = 'hubspoke' - # Set columns and width ratio. - ncols = 1 - ngroups = len(idx) - widthratio = [1] - - if ngroups > 2: - paired = False - float_contrast = False - # flatten out idx. - all_plot_groups = np.unique([t for t in idx]).tolist() - # Place idx into tuple. - idx = (idx,) - - - elif all([isinstance(i, (tuple, list)) for i in idx]): - plottype = 'multigroup' - all_plot_groups = np.unique([tt for t in idx for tt in t]).tolist() - widthratio = [len(ii) for ii in idx] - if [True for i in widthratio if i > 2]: - paired = False - float_contrast = False - # Set columns and width ratio. - ncols = len(idx) - ngroups = len(all_plot_groups) - - - else: # mix of string and tuple? - err = 'There seems to be a problem with the idx you' - 'entered--{}.'.format(idx) - raise ValueError(err) - - - # Sanity checks. - if (color_col is not None) and (color_col not in data_in.columns): - err = ' '.join(['The specified `color_col`', - '{} is not a column in `data`.'.format(color_col)]) - raise IndexError(err) - - if x is None and y is not None: - err = 'You have only specified `y`. Please also specify `x`.' - raise ValueError(err) - - elif y is None and x is not None: - err = 'You have only specified `x`. Please also specify `y`.' - raise ValueError(err) - - elif x is not None and y is not None: - # Assume we have a long dataset. - # check both x and y are column names in data. - if x not in data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(x) - raise IndexError(err) - if y not in data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(y) - raise IndexError(err) - # check y is numeric. - if not np.issubdtype(data_in[y].dtype, np.number): - err = '{0} is a column in `data`, but it is not numeric.'.format(y) - raise ValueError(err) - # check all the idx can be found in data_in[x] - for g in all_plot_groups: - if g not in data_in[x].unique(): - raise IndexError('{0} is not a group in `{1}`.'.format(g, x)) - - elif x is None and y is None: - # Assume we have a wide dataset. - # First, check we have all columns in the dataset. - for g in all_plot_groups: - if g not in data_in.columns: - raise IndexError('{0} is not a column in `data`.'.format(g)) - - # Melt it so it is easier to use. - # Preliminaries before we melt the dataframe. - x = 'group' - if swarm_label is None: - y = 'value' - else: - y = str(swarm_label) - - # Extract only the columns being plotted. - if color_col is None: - idv = ['index'] - turn_to_cat = [x] - data_in = data_in[all_plot_groups].copy() - else: - idv = ['index', color_col] - turn_to_cat = [x, color_col] - plot_groups_with_color = unpack_and_add(all_plot_groups, color_col) - data_in = data_in[plot_groups_with_color].copy() - - data_in = pd.melt(data_in.reset_index(), - id_vars=idv, - value_vars=all_plot_groups, - value_name=y, - var_name=x) - - for c in turn_to_cat: - data_in.loc[:,c] = pd.Categorical(data_in[c], - categories=data_in[c].unique(), - ordered=True) - - - # Set default kwargs first, then merge with user-dictated ones. - default_swarmplot_kwargs = {'size': raw_marker_size} - if swarmplot_kwargs is None: - swarmplot_kwargs = default_swarmplot_kwargs - else: - swarmplot_kwargs = merge_two_dicts(default_swarmplot_kwargs, - swarmplot_kwargs) - - - # Violinplot kwargs. - default_violinplot_kwargs={'widths':0.5, 'vert':True, - 'showextrema':False, 'showmedians':False} - if violinplot_kwargs is None: - violinplot_kwargs = default_violinplot_kwargs - else: - violinplot_kwargs = merge_two_dicts(default_violinplot_kwargs, - violinplot_kwargs) - - - # Zero reference-line kwargs. - default_reflines_kwargs = {'linestyle':'solid', 'linewidth':0.75, - 'color':'k'} - if reflines_kwargs is None: - reflines_kwargs = default_reflines_kwargs - else: - reflines_kwargs = merge_two_dicts(default_reflines_kwargs, - reflines_kwargs) - - - # Legend kwargs. - default_legend_kwargs = {'loc': 'upper left', 'frameon': False, - 'bbox_to_anchor': (0.95, 1.), 'markerscale': 2} - if legend_kwargs is None: - legend_kwargs = default_legend_kwargs - else: - legend_kwargs = merge_two_dicts(default_legend_kwargs, legend_kwargs) - - - # Aesthetic kwargs for sns.set(). - default_aesthetic_kwargs={'context': plot_context, 'style': 'ticks', - 'font_scale': font_scale, - 'rc': {'axes.linewidth': 1}} - if aesthetic_kwargs is None: - aesthetic_kwargs = default_aesthetic_kwargs - else: - aesthetic_kwargs = merge_two_dicts(default_aesthetic_kwargs, - aesthetic_kwargs) - - - # if paired is False, set show_pairs as False. - if paired is False: - show_pairs = False - - gs_default = {'mean_sd', 'median_quartiles', 'None'} - if group_summaries not in {'mean_sd', 'median_quartiles', 'None'}: - raise ValueError('group_summaries must be one of' - ' these: {}.'.format(gs_default) ) - - default_group_summary_kwargs = {'zorder': 5, 'lw': 2, - 'color': 'k','alpha': 1} - if group_summary_kwargs is None: - group_summary_kwargs = default_group_summary_kwargs - else: - group_summary_kwargs = merge_two_dicts(default_group_summary_kwargs, - group_summary_kwargs) - - # Plot standardized effect sizes / ordinal effect sizes on non-floating axes. - _es = ['mean_diff', 'cohens_d', 'hedges_g', 'median_diff', 'cliffs_delta'] - labels = ['Mean\ndifference', "Cohen's d", "Hedges' g", - 'Median\ndifference', "Cliff's delta"] - if effect_size not in _es: - err1 = "{} is not a plottable effect size. ".format(effect_size) - err2 = "Acceptable effect sizes are: {}".format(_es) - raise ValueError(err1 + err2) - if effect_size in ['cliffs_delta', 'cohens_d', 'hedges_g']: - float_contrast = False - dict_effect_size_label = dict(zip(_es, labels)) - effect_size_label = dict_effect_size_label[effect_size] - - # Check to ensure that line summaries for means will not be shown - # if `float_contrast` is True. - if float_contrast is True and group_summaries != 'None': - group_summaries = 'None' - - # Calculate the CI from alpha. - if ci < 0 or ci > 100: - raise ValueError('`ci` should be between 0 and 100.') - alpha_level = (100.-int(ci)) / 100. - - # Calculate the swarmplot ylims. - if swarm_ylim is None: - # To ensure points at the limits are clearly seen. - pad = data_in[y].diff().abs().min() * 2 - if pad < 3: - pad = 3 - swarm_ylim = (np.floor(data_in[y].min() - pad), - np.ceil(data_in[y].max() + pad)) - - # Set appropriate vertical spacing between subplots, - # based on whether the contrast is floating. - if float_contrast is False: - hs = cumming_vertical_spacing - else: - hs = 0 - - # Infer the figsize. - if color_col is None: - legend_xspan = 0 - else: - legend_xspan = 1.5 - - if float_contrast is True: - height_inches = 4 - width_inches = 3.5 * ncols + legend_xspan - else: - height_inches = 6 - width_inches = 1.5 * ngroups + legend_xspan - - - - fsize = (width_inches, height_inches) - if fig_size is None: - fig_size = fsize - - - # Create color palette that will be shared across subplots. - if color_col is None: - color_groups = data_in[x].unique() - else: - color_groups = data_in[color_col].unique() - - if custom_palette is None: - plotPal = dict(zip(color_groups, - sns.color_palette(n_colors=len(color_groups)) - ) - ) - else: - if isinstance(custom_palette, dict): - # check that all the keys in custom_palette are found in the - # color column. - col_grps = {k for k in color_groups} - pal_grps = {k for k in custom_palette.keys()} - not_in_pal = pal_grps.difference(col_grps) - if len(not_in_pal) > 0: - err1 = 'The custom palette keys {} '.format(not_in_pal) - err2 = 'are not found in `{}`. Please check.'.format(color_col) - errstring = (err1 + err2) - raise IndexError(errstring) - plotPal = custom_palette - - elif isinstance(custom_palette, list): - n_groups = len(color_groups) - plotPal = dict(zip(color_groups, custom_palette[0: n_groups])) - - elif isinstance(custom_palette, str): - # check it is in the list of matplotlib palettes. - if custom_palette in mpl.pyplot.colormaps(): - plotPal = custom_palette - else: - err1 = 'The specified `custom_palette` {}'.format(custom_palette) - err2 = ' is not a matplotlib palette. Please check.' - raise ValueError(err1 + err2) - - - # Create lists to store legend handles and labels for proper legend generation. - legend_handles = [] - legend_labels = [] - - - # Create list to store the bootstrap confidence interval results. - bootlist = list() - - - # Create the figure. - # Set clean style. - sns.set(**aesthetic_kwargs) - - if float_contrast is True: - fig, axx = plt.subplots(ncols=ncols, figsize=fig_size, dpi=dpi, - gridspec_kw={'width_ratios': widthratio, - 'wspace' : 1.}) - - else: - fig, axx = plt.subplots(ncols=ncols, nrows=2, figsize=fig_size, dpi=dpi, - gridspec_kw={'width_ratios': widthratio, - 'wspace' : 0}) - - # If the contrast axes are NOT floating, create lists to store raw ylims - # and raw tick intervals, so that I can normalize their ylims later. - contrast_ax_ylim_low = list() - contrast_ax_ylim_high = list() - contrast_ax_ylim_tickintervals = list() - - - # Plot each tuple in idx. - for j, current_tuple in enumerate(idx): - plotdat = data_in[data_in[x].isin(current_tuple)].copy() - plotdat.loc[:,x] = pd.Categorical(plotdat[x], - categories=current_tuple, - ordered=True) - plotdat.sort_values(by=[x]) - # Compute Ns per group. - counts = plotdat.groupby(x)[y].count() - - if float_contrast is True: - if ncols == 1: - ax_raw = axx - else: - ax_raw = axx[j] - ax_contrast = ax_raw.twinx() - else: - if ncols == 1: - ax_raw = axx[0] - ax_contrast = axx[1] - else: - ax_raw = axx[0, j] # the swarm axes are always on row 0. - ax_contrast = axx[1, j] # the contrast axes are always on row 1. - - # if float_contrast: - # ax_contrast = ax_raw.twinx() - # else: - # ax_contrast = axx[1, j] # the contrast axes are always on row 1. - # divider = make_axes_locatable(ax_raw) - # ax_contrast = divider.append_axes("bottom", size="100%", - # pad=0.5, sharex=ax_raw) - - - # Plot the raw data. - if (paired is True and show_pairs is True): - # Sanity checks. Do we have 2 elements (no more, no less) here? - if len(current_tuple) != 2: - err1 = 'Paired plotting is True, ' - err2 = 'but {0} does not have 2 elements.'.format(current_tuple) - raise ValueError(err1 + err2) - - # Are the groups equal in length?? - before = plotdat[plotdat[x] == current_tuple[0]][y].dropna().tolist() - after = plotdat[plotdat[x] == current_tuple[1]][y].dropna().tolist() - if len(before) != len(after): - err1 = 'The sizes of {} '.format(current_tuple[0]) - err2 = 'and {} do not match.'.format(current_tuple[1]) - raise ValueError(err1 + err2) - - if color_col is not None: - colors = plotdat[plotdat[x] == current_tuple[0]][color_col] - else: - plotPal['__default_black__'] = (0., 0., 0.) # black - colors = np.repeat('__default_black__',len(before)) - linedf = pd.DataFrame({str(current_tuple[0]):before, - str(current_tuple[1]):after, - 'colors':colors}) - - # Slopegraph for paired raw data points. - for ii in linedf.index: - ax_raw.plot([0, 1], # x1, x2 - [linedf.loc[ii,current_tuple[0]], - linedf.loc[ii,current_tuple[1]]] , # y1, y2 - linestyle='solid', linewidth = 1, - color = plotPal[linedf.loc[ii, 'colors']], - label = linedf.loc[ii, 'colors']) - ax_raw.set_xticks([0, 1]) - ax_raw.set_xlim(-0.25, 1.5) - ax_raw.set_xticklabels([current_tuple[0], current_tuple[1]]) - swarm_ylim = ax_raw.get_ylim() - - elif (paired is True and show_pairs is False) or (paired is False): - # Swarmplot for raw data points. - if swarm_ylim is not None: - ax_raw.set_ylim(swarm_ylim) - sns.swarmplot(data=plotdat, x=x, y=y, ax=ax_raw, - order=current_tuple, hue=color_col, - palette=plotPal, zorder=3, **swarmplot_kwargs) - if swarm_ylim is None: - swarm_ylim = ax_raw.get_ylim() - - if group_summaries != 'None': - # Create list to gather xspans. - xspans = [] - for jj, c in enumerate(ax_raw.collections): - try: - _, x_max, _, _ = get_swarm_spans(c) - x_max_span = x_max - jj - xspans.append(x_max_span) - except TypeError: - # we have got a None, so skip and move on. - pass - gapped_lines(plotdat, x=x, y=y, - # Hardcoded offset... - offset=np.max(xspans) + 0.1, - type=group_summaries, - ax=ax_raw, **group_summary_kwargs) - - ax_raw.set_xlabel('') - - - # Set new tick labels. The tick labels belong to the SWARM axes - # for both floating and non-floating plots. - # This is because `sharex` was invoked. - xticklabels = list() - - for xticklab in ax_raw.xaxis.get_ticklabels(): - t = xticklab.get_text() - N = str(counts.ix[t]) - if show_group_count: - xticklabels.append(t+' n='+N) - else: - xticklabels.append(t) - if float_contrast is True: - ax_raw.set_xticklabels(xticklabels, rotation=45, - horizontalalignment='right') - - - # Despine appropriately. - if float_contrast: - sns.despine(ax=ax_raw, trim=True) - else: - ax_raw.xaxis.set_visible(False) - not_first_ax = (j != 0) - sns.despine(ax=ax_raw, bottom=True, left=not_first_ax, trim=True) - if not_first_ax: - ax_raw.yaxis.set_visible(False) - - - # Save the handles and labels for the legend. - handles,labels = ax_raw.get_legend_handles_labels() - for l in labels: - legend_labels.append(l) - for h in handles: - legend_handles.append(h) - if color_col is not None: - ax_raw.legend().set_visible(False) - # Make sure we can easily pull out the right-most raw swarm axes. - if j + 1 == ncols: - last_swarm = ax_raw - - - # Plot the contrast data. - ref = np.array(plotdat[plotdat[x] == current_tuple[0]][y].dropna()) - for ix, grp in enumerate(current_tuple[1:]): - # add spacer to halfviolin if float_contast is true. - if float_contrast is True: - if paired is True and show_pairs is True: - spacer = 0.5 - else: - spacer = 0.75 - else: - spacer = 0 - pos = ix + spacer - - # Calculate bootstrapped stats. - exp = np.array(plotdat[plotdat[x] == grp][y].dropna()) - results = difference_ci(ref, exp, is_paired=paired, - alpha=alpha_level, resamples=n_boot, - random_seed=random_seed) - res = {} - res['reference_group'] = current_tuple[0] - res['experimental_group'] = grp - - # Parse results into dict. - for _es_ in results.index: - res[_es_] = results.loc[_es_,'effect_size'] - - es_ci_low = '{}_ci_low'.format(_es_) - res[es_ci_low] = results.loc[_es_,'bca_ci_low'] - - es_ci_high = '{}_ci_high'.format(_es_) - res[es_ci_high] = results.loc[_es_,'bca_ci_high'] - - es_bootstraps = '{}_bootstraps'.format(_es_) - res[es_bootstraps] = results.loc[_es_,'bootstraps'] - - if paired: - res['paired'] = True - res['pvalue_paired_ttest'] = ttest_rel(ref, exp).pvalue - res['pvalue_mann_whitney'] = mannwhitneyu(ref, exp).pvalue - else: - res['paired'] = False - res['pvalue_ind_ttest'] = ttest_ind(ref, exp).pvalue - res['pvalue_wilcoxon'] = wilcoxon(ref, exp).pvalue - - bootlist.append(res) - - # Figure out what to plot based on desired effect size. - bootstraps = res['{}_bootstraps'.format(effect_size)] - es = res[effect_size] - ci_low = res['{}_ci_low'.format(effect_size)] - ci_high = res['{}_ci_high'.format(effect_size)] - - # Plot the halfviolin and mean+CIs on contrast axes. - v = ax_contrast.violinplot(bootstraps, positions=[pos+1], - **violinplot_kwargs) - halfviolin(v) # Turn the violinplot into half. - # Plot the effect size. - ax_contrast.plot([pos+1], es, marker='o', color='k', - markersize=es_marker_size) - # Plot the confidence interval. - ax_contrast.plot([pos+1, pos+1], [ci_low, ci_high], - 'k-', linewidth=group_summary_kwargs['lw']) - - if float_contrast is False: - l, h = ax_contrast.get_ylim() - contrast_ax_ylim_low.append(l) - contrast_ax_ylim_high.append(h) - ticklocs = ax_contrast.yaxis.get_majorticklocs() - new_interval = ticklocs[1] - ticklocs[0] - contrast_ax_ylim_tickintervals.append(new_interval) - - if float_contrast is False: - ax_contrast.set_xlim(ax_raw.get_xlim()) - ax_contrast.set_xticks(ax_raw.get_xticks()) - ax_contrast.set_xticklabels(xticklabels, rotation=45, - horizontalalignment='right') - - else: # float_contrast is True - if effect_size == 'mean_diff': - _e = np.mean(exp) - elif effect_size == 'median_diff': - _e = np.median(exp) - - # Normalize ylims and despine the floating contrast axes. - # Check that the effect size is within the swarm ylims. - min_check = swarm_ylim[0] - _e - max_check = swarm_ylim[1] - _e - if (min_check <= es <= max_check) == False: - err1 = 'The mean of the reference group {} does not '.format(_e) - err2 = 'fall in the specified `swarm_ylim` {}. '.format(swarm_ylim) - err3 = 'Please select a `swarm_ylim` that includes the ' - err4 = 'reference mean, or set `float_contrast=False`.' - err = err1 + err2 + err3 + err4 - raise ValueError(err) - - # Align 0 of ax_contrast to reference group mean of ax_raw. - ylimlow, ylimhigh = ax_contrast.get_xlim() - ax_contrast.set_xlim(ylimlow, ylimhigh + spacer) - - # If the effect size is positive, shift the contrast axis up. - if es > 0: - rightmin, rightmax = np.array(ax_raw.get_ylim()) - es - # If the effect size is negative, shift the contrast axis down. - elif es < 0: - rightmin, rightmax = np.array(ax_raw.get_ylim()) + es - - ax_contrast.set_ylim(rightmin, rightmax) - - # align statfunc(exp) on ax_raw with the effect size on ax_contrast. - align_yaxis(ax_raw, _e, ax_contrast, es) - - # Draw zero line. - xlimlow, xlimhigh = ax_contrast.get_xlim() - ax_contrast.hlines(0, # y-coordinates - 0, xlimhigh, # x-coordinates, start and end. - **reflines_kwargs) - - # Draw effect size line. - ax_contrast.hlines(es, - 1, xlimhigh, # x-coordinates, start and end. - **reflines_kwargs) - - # Shrink or stretch axis to encompass 0 and min/max contrast. - # Get the lower and upper limits. - lower = bootstraps.min() - upper = bootstraps.max() - - # Make sure we have zero in the limits. - if lower > 0: - lower = 0. - if upper < 0: - upper = 0. - - # Get the tick interval from the left y-axis. - leftticks = ax_contrast.get_yticks() - tickstep = leftticks[1] -leftticks[0] - - # First re-draw of axis with new tick interval - new_locator = tk.MultipleLocator(base=tickstep) - ax_contrast.yaxis.set_major_locator(new_locator) - newticks1 = ax_contrast.get_yticks() - - # Obtain major ticks that comfortably encompass lower and upper. - newticks2 = list() - for a, b in enumerate(newticks1): - if (b >= lower and b <= upper): - # if the tick lies within upper and lower, take it. - newticks2.append(b) - - # If the effect size falls outside of the newticks2 set, - # add a tick in the right direction. - if np.max(newticks2) < es: - # find out the max tick index in newticks1. - ind = np.where(newticks1 == np.max(newticks2))[0][0] - newticks2.append(newticks1[ind + 1]) - elif es < np.min(newticks2): - # find out the min tick index in newticks1. - ind = np.where(newticks1 == np.min(newticks2))[0][0] - newticks2.append(newticks1[ind - 1]) - newticks2 = np.array(newticks2) - newticks2.sort() - - # Re-draw axis to shrink it to desired limits. - locc = tk.FixedLocator(locs=newticks2) - ax_contrast.yaxis.set_major_locator(locc) - - # Despine the axes. - sns.despine(ax=ax_contrast, trim=True, - # remove the left and bottom spines... - left=True, bottom=True, - # ...but not the right spine. - right=False) - - - # Set the y-axis labels. - if j > 0: - ax_raw.set_ylabel('', labelpad=tick_length) - else: - ax_raw.set_ylabel(y, labelpad=tick_length) - - if float_contrast is False: - if j > 0: - ax_contrast.set_ylabel('', labelpad=tick_length) - else: - if contrast_label is None: - if paired: - ax_contrast.set_ylabel('paired \n' + effect_size_label, - labelpad=tick_length) - else: - ax_contrast.set_ylabel(effect_size_label, - labelpad=tick_length) - else: - ax_contrast.set_ylabel(str(contrast_label), - labelpad=tick_length) - - # ROTATE X-TICKS OF ax_contrast - rotate_ticks(ax_contrast, angle=45, alignment='right') - - - # Equalize the ylims across subplots. - if float_contrast is False: - # Sort and convert to numpy arrays. - contrast_ax_ylim_low = np.sort(contrast_ax_ylim_low) - contrast_ax_ylim_high = np.sort(contrast_ax_ylim_high) - contrast_ax_ylim_tickintervals = np.sort(contrast_ax_ylim_tickintervals) - - # Compute normalized ylim, or set normalized ylim to desired ylim. - if contrast_ylim is None: - normYlim = (contrast_ax_ylim_low[0], contrast_ax_ylim_high[-1]) - else: - normYlim = contrast_ylim - - # Loop thru the contrast axes again to re-draw all the y-axes. - for i in range(ncols, ncols*2, 1): - # The last half of the axes in `fig` are the contrast axes. - axx = fig.get_axes()[i] - - # Set the axes to the max ylim. - axx.set_ylim(normYlim[0], normYlim[1]) - - # Draw zero reference line if zero is in the ylim range. - if normYlim[0] < 0. and 0. < normYlim[1]: - axx.axhline(y=0, lw=0.5, color='k') - - # Hide the y-axis except for the leftmost contrast axes. - if i > ncols: - axx.get_yaxis().set_visible(False) - sns.despine(ax=axx, left=True, trim=True) - else: - # Despine. - sns.despine(ax=axx, trim=True) - - - # Add Figure Legend. - if color_col is not None: - legend_labels_unique = np.unique(legend_labels) - unique_idx = np.unique(legend_labels, return_index=True)[1] - legend_handles_unique = (pd.Series(legend_handles).loc[unique_idx]).tolist() - leg = last_swarm.legend(legend_handles_unique, legend_labels_unique, - **legend_kwargs) - - if paired is True and show_pairs is True: - for line in leg.get_lines(): - line.set_linewidth(3.0) - - - # Turn `bootlist` into a pandas DataFrame - bootlist_df = pd.DataFrame(bootlist) - - - # Order the columns properly. - cols = bootlist_df.columns.tolist() - - move_to_front = ['reference_group', 'experimental_group', 'paired'] - mean_diff_cols = ['mean_diff', 'mean_diff_bootstraps', - 'mean_diff_ci_high', 'mean_diff_ci_low'] - for c in move_to_front + mean_diff_cols: - cols.remove(c) - new_order_cols = move_to_front + mean_diff_cols + cols - bootlist_df = bootlist_df[new_order_cols] - - # Remove unused columns. - bootlist_df = bootlist_df.replace(to_replace='NIL', - value=np.nan).dropna(axis=1) - - - # Reset seaborn aesthetic parameters. - sns.set() - - - # Set custom swarm label if so desired. - if swarm_label is not None: - fig.axes[0].set_ylabel(swarm_label) - - - # Lengthen the axes ticks so they look better. - for ax in fig.axes: - ax.tick_params(length=tick_length, pad=tick_pad, width=1) - - - # Remove the background from all the axes. - for ax in fig.axes: - ax.patch.set_visible(False) - - - # Return the figure and the results DataFrame. - return fig, bootlist_df diff --git a/dabest/_archive/bootstrap_tools.py b/dabest/_archive/bootstrap_tools.py deleted file mode 100644 index ef24c6e9..00000000 --- a/dabest/_archive/bootstrap_tools.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com - - -from __future__ import division - - -class bootstrap: - '''Computes the summary statistic and a bootstrapped confidence interval. - - Keywords: - x1, x2: array-like - The data in a one-dimensional array form. Only x1 is required. - If x2 is given, the bootstrapped summary difference between - the two groups (x2-x1) is computed. - NaNs are automatically discarded. - - paired: boolean, default False - Whether or not x1 and x2 are paired samples. - - statfunction: callable, default np.mean - The summary statistic called on data. - - smoothboot: boolean, default False - Taken from seaborn.algorithms.bootstrap. - If True, performs a smoothed bootstrap (draws samples from a kernel - destiny estimate). - - alpha: float, default 0.05 - Denotes the likelihood that the confidence interval produced - _does not_ include the true summary statistic. When alpha = 0.05, - a 95% confidence interval is produced. - - reps: int, default 5000 - Number of bootstrap iterations to perform. - - Returns: - An `bootstrap` object reporting the summary statistics, percentile CIs, - bias-corrected and accelerated (BCa) CIs, and the settings used. - - summary: float - The summary statistic. - - is_difference: boolean - Whether or not the summary is the difference between two groups. - If False, only x1 was supplied. - - is_paired: boolean - Whether or not the difference reported is between 2 paired groups. - - statistic: callable - The function used to compute the summary. - - reps: int - The number of bootstrap iterations performed. - - stat_array: array. - A sorted array of values obtained by bootstrapping the input arrays. - - ci: float - The size of the confidence interval reported (in percentage). - - pct_ci_low, pct_ci_high: floats - The upper and lower bounds of the confidence interval as computed - by taking the percentage bounds. - - pct_low_high_indices: array - An array with the indices in `stat_array` corresponding to the - percentage confidence interval bounds. - - bca_ci_low, bca_ci_high: floats - The upper and lower bounds of the bias-corrected and accelerated - (BCa) confidence interval. See Efron 1977. - - bca_low_high_indices: array - An array with the indices in `stat_array` corresponding to the BCa - confidence interval bounds. - - pvalue_1samp_ttest: float - P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were - passed (x1 and x2), returns 'NIL'. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_1samp.html - - pvalue_2samp_ind_ttest: float - P-value obtained from scipy.stats.ttest_ind. - If a single array was given (x1 only), or if `paired` is True, - returns 'NIL'. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_ind.html - - pvalue_2samp_related_ttest: float - P-value obtained from scipy.stats.ttest_rel. - If a single array was given (x1 only), or if `paired` is False, - returns 'NIL'. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_rel.html - - pvalue_wilcoxon: float - P-value obtained from scipy.stats.wilcoxon. - If a single array was given (x1 only), or if `paired` is False, - returns 'NIL'. - The Wilcoxons signed-rank test is a nonparametric paired test of - the null hypothesis that the related samples x1 and x2 are from - the same distribution. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/scipy.stats.wilcoxon.html - - pvalue_mann_whitney: float - Two-sided p-value obtained from scipy.stats.mannwhitneyu. - If a single array was given (x1 only), returns 'NIL'. - The Mann-Whitney U-test is a nonparametric unpaired test of the null - hypothesis that x1 and x2 are from the same distribution. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.mannwhitneyu.html - - ''' - def __init__(self, x1, x2=None, - paired=False, - statfunction=None, - smoothboot=False, - alpha_level=0.05, - reps=5000): - - import numpy as np - import pandas as pd - import seaborn as sns - - from scipy.stats import norm - from numpy.random import randint - from scipy.stats import ttest_1samp, ttest_ind, ttest_rel - from scipy.stats import mannwhitneyu, wilcoxon, norm - import warnings - - # Turn to pandas series. - x1 = pd.Series(x1).dropna() - diff = False - - # Initialise statfunction - if statfunction == None: - statfunction = np.mean - - # Compute two-sided alphas. - if alpha_level > 1. or alpha_level < 0.: - raise ValueError("alpha_level must be between 0 and 1.") - alphas = np.array([alpha_level/2., 1-alpha_level/2.]) - - sns_bootstrap_kwargs = {'func': statfunction, - 'n_boot': reps, - 'smooth': smoothboot} - - if paired: - # check x2 is not None: - if x2 is None: - raise ValueError('Please specify x2.') - else: - x2 = pd.Series(x2).dropna() - if len(x1) != len(x2): - raise ValueError('x1 and x2 are not the same length.') - - if (x2 is None) or (paired is True) : - - if x2 is None: - tx = x1 - paired = False - ttest_single = ttest_1samp(x1, 0)[1] - ttest_2_ind = 'NIL' - ttest_2_paired = 'NIL' - wilcoxonresult = 'NIL' - - elif paired is True: - diff = True - tx = x2 - x1 - ttest_single = 'NIL' - ttest_2_ind = 'NIL' - ttest_2_paired = ttest_rel(x1, x2)[1] - wilcoxonresult = wilcoxon(x1, x2)[1] - mannwhitneyresult = 'NIL' - - # Turns data into array, then tuple. - tdata = (tx,) - - # The value of the statistic function applied - # just to the actual data. - summ_stat = statfunction(*tdata) - statarray = sns.algorithms.bootstrap(tx, **sns_bootstrap_kwargs) - statarray.sort() - - # Get Percentile indices - pct_low_high = np.round((reps-1) * alphas) - pct_low_high = np.nan_to_num(pct_low_high).astype('int') - - - elif x2 is not None and paired is False: - diff = True - x2 = pd.Series(x2).dropna() - # Generate statarrays for both arrays. - ref_statarray = sns.algorithms.bootstrap(x1, **sns_bootstrap_kwargs) - exp_statarray = sns.algorithms.bootstrap(x2, **sns_bootstrap_kwargs) - - tdata = exp_statarray - ref_statarray - statarray = tdata.copy() - statarray.sort() - tdata = (tdata, ) # Note tuple form. - - # The difference as one would calculate it. - summ_stat = statfunction(x2) - statfunction(x1) - - # Get Percentile indices - pct_low_high = np.round((reps-1) * alphas) - pct_low_high = np.nan_to_num(pct_low_high).astype('int') - - # Statistical tests. - ttest_single='NIL' - ttest_2_ind = ttest_ind(x1,x2)[1] - ttest_2_paired='NIL' - mannwhitneyresult = mannwhitneyu(x1, x2, alternative='two-sided')[1] - wilcoxonresult = 'NIL' - - # Get Bias-Corrected Accelerated indices convenience function invoked. - bca_low_high = bca(tdata, alphas, statarray, - statfunction, summ_stat, reps) - - # Warnings for unstable or extreme indices. - for ind in [pct_low_high, bca_low_high]: - if np.any(ind == 0) or np.any(ind == reps-1): - warnings.warn("Some values used extremal samples;" - " results are probably unstable.") - elif np.any(ind<10) or np.any(ind>=reps-10): - warnings.warn("Some values used top 10 low/high samples;" - " results may be unstable.") - - self.summary = summ_stat - self.is_paired = paired - self.is_difference = diff - self.statistic = str(statfunction) - self.n_reps = reps - - self.ci = (1-alpha_level)*100 - self.stat_array = np.array(statarray) - - self.pct_ci_low = statarray[pct_low_high[0]] - self.pct_ci_high = statarray[pct_low_high[1]] - self.pct_low_high_indices = pct_low_high - - self.bca_ci_low = statarray[bca_low_high[0]] - self.bca_ci_high = statarray[bca_low_high[1]] - self.bca_low_high_indices = bca_low_high - - self.pvalue_1samp_ttest = ttest_single - self.pvalue_2samp_ind_ttest = ttest_2_ind - self.pvalue_2samp_paired_ttest = ttest_2_paired - self.pvalue_wilcoxon = wilcoxonresult - self.pvalue_mann_whitney = mannwhitneyresult - - self.results = {'stat_summary': self.summary, - 'is_difference': diff, - 'is_paired': paired, - 'bca_ci_low': self.bca_ci_low, - 'bca_ci_high': self.bca_ci_high, - 'ci': self.ci - } - - def __repr__(self): - import numpy as np - - if 'mean' in self.statistic: - stat = 'mean' - elif 'median' in self.statistic: - stat = 'median' - else: - stat = self.statistic - - diff_types = {True: 'paired', False: 'unpaired'} - if self.is_difference: - a = 'The {} {} difference is {}.'.format(diff_types[self.is_paired], - stat, self.summary) - else: - a = 'The {} is {}.'.format(stat, self.summary) - - b = '[{} CI: {}, {}]'.format(self.ci, self.bca_ci_low, self.bca_ci_high) - return '\n'.join([a, b]) diff --git a/dabest/_archive/old_test_bootstrap.py b/dabest/_archive/old_test_bootstrap.py deleted file mode 100644 index c52d57ff..00000000 --- a/dabest/_archive/old_test_bootstrap.py +++ /dev/null @@ -1,225 +0,0 @@ -# #! /usr/bin/env python -import pytest -import sys -import numpy as np -import scipy as sp - -# This filters out an innocuous warning when pandas is imported, -# but the version has not been compiled against the newest numpy. -import warnings -warnings.filterwarnings("ignore", message="numpy.dtype size changed") - -import pandas as pd -from .. import _bootstrap_tools as bst - - - -def create_dummy_dataset(seed=None, n=30, base_mean=0, expt_groups=6, - scale_means=2, scale_std=1.2): - """ - Creates a dummy dataset for plotting. - - Returns the seed used to generate the random numbers, - the maximum possible difference between mean differences, - and the dataset itself. - """ - - # Set a random seed. - if seed is None: - random_seed = np.random.randint(low=1, high=1000, size=1)[0] - else: - if isinstance(seed, int): - random_seed = seed - else: - raise TypeError('{} is not an integer.'.format(seed)) - - # Generate a set of random means - np.random.seed(random_seed) - MEANS = np.repeat(base_mean, expt_groups) + np.random.random(size=expt_groups) * scale_means - SCALES = np.random.random(size=expt_groups) * scale_std - - max_mean_diff = np.ptp(MEANS) - - dataset = list() - for i, m in enumerate(MEANS): - pop = sp.stats.norm.rvs(loc=m, scale=SCALES[i], size=10000) - sample = np.random.choice(pop, size=n, replace=False) - dataset.append(sample) - - df = pd.DataFrame(dataset).T - df.columns = [str(c) for c in df.columns] - - return random_seed, max_mean_diff, df - - -def is_difference(result): - assert result.is_difference == True - -def is_paired(result): - assert result.is_paired == True - -def check_pvalue_1samp(result): - assert result.pvalue_1samp_ttest != 'NIL' - -def check_pvalue_2samp_unpaired(result): - assert result.pvalue_2samp_ind_ttest != 'NIL' - -def check_pvalue_2samp_paired(result): - assert result.pvalue_2samp_related_ttest != 'NIL' - -def check_mann_whitney(result): - """Nonparametric unpaired""" - assert result.pvalue_mann_whitney != 'NIL' - assert result.pvalue_wilcoxon == 'NIL' - -def check_wilcoxon(result): - """Nonparametric Paired""" - assert result.pvalue_wilcoxon != 'NIL' - assert result.pvalue_mann_whitney == 'NIL' - -# def test_mean_within_ci_bca(mean, result): -# assert mean >= result.bca_ci_low -# assert mean <= result.bca_ci_high -# -# def test_mean_within_ci_pct(mean, result): -# assert mean >= result.pct_ci_low -# assert mean <= result.pct_ci_high - -def single_samp_stat_tests(sample, result): - - assert result.is_difference == False - assert result.is_paired == False - - ttest_result = sp.stats.ttest_1samp(sample, 0).pvalue - assert result.pvalue_1samp_ttest == pytest.approx(ttest_result) - -def unpaired_stat_tests(control, expt, result): - is_difference(result) - check_pvalue_2samp_unpaired(result) - check_mann_whitney(result) - - true_mean = expt.mean() - control.mean() - assert result.summary == pytest.approx(true_mean) - - scipy_ttest_ind_result = sp.stats.ttest_ind(control, expt).pvalue - assert result.pvalue_2samp_ind_ttest == pytest.approx(scipy_ttest_ind_result) - - mann_whitney_result = sp.stats.mannwhitneyu(control, expt, - alternative='two-sided').pvalue - assert result.pvalue_mann_whitney == pytest.approx(mann_whitney_result) - -def paired_stat_tests(control, expt, result): - is_difference(result) - is_paired(result) - check_wilcoxon(result) - - true_mean = np.mean(expt - control) - assert result.summary == pytest.approx(true_mean) - - scipy_ttest_paired = sp.stats.ttest_rel(control, expt).pvalue - assert result.pvalue_2samp_paired_ttest == pytest.approx(scipy_ttest_paired) - - wilcoxon_result = sp.stats.wilcoxon(control, expt).pvalue - assert result.pvalue_wilcoxon == pytest.approx(wilcoxon_result) - -def does_ci_capture_mean_diff(control, expt, paired, nreps=100, alpha=0.05): - if expt is None: - mean_diff = control.mean() - else: - if paired is True: - mean_diff = np.mean(expt - control) - elif paired is False: - mean_diff = expt.mean() - control.mean() - - ERROR_THRESHOLD = nreps * alpha - error_count_bca = 0 - error_count_pct = 0 - - for i in range(1, nreps): - results = bst.bootstrap(control, expt, paired=paired, alpha_level=alpha) - - print("\n95CI BCa = {}, {}".format(results.bca_ci_low, results.bca_ci_high)) - try: - # test_mean_within_ci_bca(mean_diff, results) - assert mean_diff >= results.bca_ci_low - assert mean_diff <= results.bca_ci_high - except AssertionError: - error_count_bca += 1 - - print("\n95CI %tage = {}, {}".format(results.pct_ci_low, results.pct_ci_high)) - try: - # test_mean_within_ci_pct(mean_diff, results) - assert mean_diff >= results.pct_ci_low - assert mean_diff <= results.pct_ci_high - except AssertionError: - error_count_pct += 1 - - print('\nNumber of BCa CIs not capturing the mean is {}'.format(error_count_bca)) - assert error_count_bca < ERROR_THRESHOLD - - print('\nNumber of Pct CIs not capturing the mean is {}'.format(error_count_pct)) - assert error_count_pct < ERROR_THRESHOLD - - - - -# Start tests below. -def test_single_sample_bootstrap(mean=100, sd=10, n=25, nreps=100, alpha=0.05): - print("Testing single sample bootstrap.") - - # Set the random seed. - random_seed = np.random.randint(low=1, high=1000, size=1)[0] - np.random.seed(random_seed) - print("\nRandom seed = {}".format(random_seed)) - - # single sample - pop = sp.stats.norm.rvs(loc=mean, scale=sd * np.random.random(1)[0], size=10000) - sample = np.random.choice(pop, size=n, replace=False) - print("\nMean = {}".format(mean)) - - results = bst.bootstrap(sample, alpha_level=alpha) - single_samp_stat_tests(sample, results) - - does_ci_capture_mean_diff(sample, None, False, nreps, alpha) - - - -def test_unpaired_difference(mean=100, sd=10, n=25, nreps=100, alpha=0.05): - print("Testing unpaired difference bootstrap.\n") - - rand_delta = np.random.randint(-10, 10) # randint between -10 and 10 - SCALES = sd * np.random.random(2) - - pop1 = sp.stats.norm.rvs(loc=mean, scale=SCALES[0], size=10000) - sample1 = np.random.choice(pop1, size=n, replace=False) - - pop2 = sp.stats.norm.rvs(loc=mean+rand_delta, scale=SCALES[1], size=10000) - sample2 = np.random.choice(pop2, size=n, replace=False) - - results = bst.bootstrap(sample1, sample2, paired=False, alpha_level=alpha) - unpaired_stat_tests(sample1, sample2, results) - - does_ci_capture_mean_diff(sample1, sample2, False, nreps, alpha) - - - -def test_paired_difference(mean=100, sd=10, n=25, nreps=100, alpha=0.05): - print("Testing paired difference bootstrap.\n") - - # Assume equal variances here, given that the samples - # are supposed to be paired. - - rand_delta = np.random.randint(-10, 10) # randint between -10 and 10 - print('difference={}'.format(rand_delta)) - SCALE = sd * np.random.random(1)[0] - - pop1 = sp.stats.norm.rvs(loc=mean, scale=SCALE, size=10000) - sample1 = np.random.choice(pop1, size=n, replace=False) - - pop2 = sp.stats.norm.rvs(loc=mean+rand_delta, scale=SCALE, size=10000) - sample2 = np.random.choice(pop2, size=n, replace=False) - - results = bst.bootstrap(sample1, sample2, alpha_level=alpha, paired=True) - paired_stat_tests(sample1, sample2, results) - - does_ci_capture_mean_diff(sample1, sample2, True, nreps, alpha) diff --git a/dabest/_archive/old_test_plotting.py b/dabest/_archive/old_test_plotting.py deleted file mode 100644 index 6df1255f..00000000 --- a/dabest/_archive/old_test_plotting.py +++ /dev/null @@ -1,171 +0,0 @@ -# #! /usr/bin/env python - -# Load Libraries -import numpy as np -import scipy as sp -import matplotlib as mpl -mpl.use('Agg') - - -import matplotlib.pyplot as plt -import pandas as pd -import seaborn as sns -import pytest -from .. import _api - - -def create_dummy_dataset(seed=None, n=30, base_mean=0, expt_groups=6, - scale_means=2, scale_std=1.2): - """ - Creates a dummy dataset for plotting. - - Returns the seed used to generate the random numbers, - the maximum possible difference between mean differences, - and the dataset itself. - """ - - # Set a random seed. - if seed is None: - random_seed = np.random.randint(low=1, high=1000, size=1)[0] - else: - if isinstance(seed, int): - random_seed = seed - else: - raise TypeError('{} is not an integer.'.format(seed)) - - # Generate a set of random means - np.random.seed(random_seed) - MEANS = np.repeat(base_mean, expt_groups) + np.random.random(size=expt_groups) * scale_means - SCALES = np.random.random(size=expt_groups) * scale_std - - max_mean_diff = np.ptp(MEANS) - - dataset = list() - for i, m in enumerate(MEANS): - pop = sp.stats.norm.rvs(loc=m, scale=SCALES[i], size=10000) - sample = np.random.choice(pop, size=n, replace=False) - dataset.append(sample) - - df = pd.DataFrame(dataset).T - df["idcol"] = pd.Series(range(1, n)) - df.columns = [str(c) for c in df.columns] - - return random_seed, max_mean_diff, df - - - -def get_swarm_yspans(coll, round_result=False, decimals=12): - """ - Given a matplotlib Collection, will obtain the y spans - for the collection. Will return None if this fails. - - Modified from `get_swarm_spans` in plot_tools.py. - """ - _, y = np.array(coll.get_offsets()).T - try: - if round_result: - return np.around(y.min(), decimals), np.around(y.max(),decimals) - else: - return y.min(), y.max() - except ValueError: - return None - - - -# Start tests. -def test_swarmspan(): - print('Testing swarmspans') - - base_mean = np.random.randint(10, 101) - seed, ptp, df = create_dummy_dataset(base_mean=base_mean) - - print('\nSeed = {}; base mean = {}'.format(seed, base_mean)) - - for c in df.columns[1:-1]: - print('{}...'.format(c)) - - f1, swarmplt = plt.subplots(1) - sns.swarmplot(data=df[[df.columns[0], c]], ax=swarmplt) - sns_yspans = [] - for coll in swarmplt.collections: - sns_yspans.append(get_swarm_yspans(coll)) - - f2, b = _api.plot(data=df, idx=(df.columns[0], c)) - dabest_yspans = [] - for coll in f2.axes[0].collections: - dabest_yspans.append(get_swarm_yspans(coll)) - - for j, span in enumerate(sns_yspans): - assert span == pytest.approx(dabest_yspans[j]) - - - -def test_ylims(): - print('Testing assignment of ylims.') - seed, ptp, df = create_dummy_dataset(base_mean=0) - ylim_min = int(np.floor(-ptp) - 1) - ylim_max = int(np.ceil(ptp) + 1) - print('seed = {}'.format(seed)) - - - print('\nTesting assignment of ylims for Cummings plot') - rand_swarm_ylim1 = (np.random.randint(ylim_min, 0), np.random.randint(ylim_max, ylim_max*2)) - rand_contrast_ylim1 = (np.random.randint(-1, 0), np.random.randint(0, 1)) - print('Swarm ylim = {}, {}'.format(rand_swarm_ylim1[0], rand_swarm_ylim1[1])) - print('Contrast ylim = {}, {}'.format(rand_contrast_ylim1[0], rand_contrast_ylim1[1])) - f1, b1 = _api.plot(data=df, - idx=(('0','1'),('2','3')), - float_contrast=False, - swarm_ylim=rand_swarm_ylim1, - contrast_ylim=rand_contrast_ylim1) - - for i in range(0, int(len(f1.axes)/2)): - assert f1.axes[i].get_ylim() == pytest.approx(rand_swarm_ylim1) - for i in range(int(len(f1.axes)/2), len(f1.axes)): - assert f1.axes[i].get_ylim() == pytest.approx(rand_contrast_ylim1) - - - print('\nTesting assignment for Gardner-Altman plot') - rand_swarm_ylim2 = (np.random.randint(ylim_min, 0), np.random.randint(ylim_max, ylim_max*2)) - print('Swarm ylim = {}, {}'.format(rand_swarm_ylim2[0], rand_swarm_ylim2[1])) - f2, b2 = _api.plot(data=df, - idx=(('0','1'),('2','3')), - float_contrast=True, - swarm_ylim=rand_swarm_ylim2) - - for i in range(0, int(len(f2.axes)/2)): - assert f2.axes[i].get_ylim() == pytest.approx(rand_swarm_ylim2) - - -def test_ylabels(): - print('Testing assignment of ylabels') - seed, ptp, df = create_dummy_dataset() - - print('Testing ylabel assignment for Gardner-Altman plot') - f1, _ = _api.plot(data=df, - idx=(('0','1'),('2','3')), - float_contrast=True, - swarm_label="Hello", - contrast_label="World" - ) - assert f1.axes[0].get_ylabel() == 'Hello' - - print('Testing ylabel assignment for Cummings plot') - f2, _ = _api.plot(data=df, - idx=(('0','1'),('2','3')), - float_contrast=False, - swarm_label="Hello Again", - contrast_label="World\nFolks" - ) - assert f2.axes[0].get_ylabel() == "Hello Again" - assert f2.axes[2].get_ylabel() == "World\nFolks" - - - -def test_paired(): - print('Testing Gardner-Altman paired plotting') - seed, ptp, df = create_dummy_dataset() - f, b = _api.plot(data=df, idx=('0','1'), paired=True, id_col="idcol") - axx = f.axes[0] - assert df['0'].tolist() == [l.get_ydata()[0] for l in axx.lines] - assert df['1'].tolist() == [l.get_ydata()[1] for l in axx.lines] diff --git a/dabest/_bootstrap_tools.py b/dabest/_bootstrap_tools.py index 261dd1d0..d04a46c8 100644 --- a/dabest/_bootstrap_tools.py +++ b/dabest/_bootstrap_tools.py @@ -1,123 +1,62 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/bootstrap.ipynb. +# %% auto 0 +__all__ = ['bootstrap', 'jackknife_indexes', 'bca'] -from __future__ import division - +# %% ../nbs/API/bootstrap.ipynb 3 +import numpy as np +# %% ../nbs/API/bootstrap.ipynb 4 class bootstrap: - '''Computes the summary statistic and a bootstrapped confidence interval. - - Keywords: - x1, x2: array-like - The data in a one-dimensional array form. Only x1 is required. - If x2 is given, the bootstrapped summary difference between - the two groups (x2-x1) is computed. - NaNs are automatically discarded. - - paired: boolean, default False - Whether or not x1 and x2 are paired samples. - - statfunction: callable, default np.mean - The summary statistic called on data. - - smoothboot: boolean, default False - Taken from seaborn.algorithms.bootstrap. - If True, performs a smoothed bootstrap (draws samples from a kernel - destiny estimate). - - alpha: float, default 0.05 - Denotes the likelihood that the confidence interval produced - does not include the true summary statistic. When alpha = 0.05, - a 95% confidence interval is produced. - - reps: int, default 5000 - Number of bootstrap iterations to perform. - - Returns: - An `bootstrap` object reporting the summary statistics, percentile CIs, - bias-corrected and accelerated (BCa) CIs, and the settings used. - - summary: float + ''' + Computes the summary statistic and a bootstrapped confidence interval. + + Returns + ------- + An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used: + `summary`: float. The summary statistic. - - is_difference: boolean - Whether or not the summary is the difference between two groups. - If False, only x1 was supplied. - - is_paired: boolean - Whether or not the difference reported is between 2 paired groups. - - statistic: callable + `is_difference`: boolean. + Whether or not the summary is the difference between two groups. If False, only x1 was supplied. + `is_paired`: string, default None + The type of the experiment under which the data are obtained + `statistic`: callable The function used to compute the summary. - - reps: int + `reps`: int The number of bootstrap iterations performed. - - stat_array: array. + `stat_array`:array A sorted array of values obtained by bootstrapping the input arrays. - - ci: float + `ci`:float The size of the confidence interval reported (in percentage). - - pct_ci_low, pct_ci_high: floats - The upper and lower bounds of the confidence interval as computed - by taking the percentage bounds. - - pct_low_high_indices: array - An array with the indices in `stat_array` corresponding to the - percentage confidence interval bounds. - - bca_ci_low, bca_ci_high: floats - The upper and lower bounds of the bias-corrected and accelerated - (BCa) confidence interval. See Efron 1977. - - bca_low_high_indices: array - An array with the indices in `stat_array` corresponding to the BCa - confidence interval bounds. - - pvalue_1samp_ttest: float - P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were - passed (x1 and x2), returns 'NIL'. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_1samp.html - - pvalue_2samp_ind_ttest: float - P-value obtained from scipy.stats.ttest_ind. - If a single array was given (x1 only), or if `paired` is True, - returns 'NIL'. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_ind.html - - pvalue_2samp_related_ttest: float - P-value obtained from scipy.stats.ttest_rel. - If a single array was given (x1 only), or if `paired` is False, - returns 'NIL'. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_rel.html - - pvalue_wilcoxon: float - P-value obtained from scipy.stats.wilcoxon. - If a single array was given (x1 only), or if `paired` is False, - returns 'NIL'. - The Wilcoxons signed-rank test is a nonparametric paired test of - the null hypothesis that the related samples x1 and x2 are from - the same distribution. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/scipy.stats.wilcoxon.html - - pvalue_mann_whitney: float - Two-sided p-value obtained from scipy.stats.mannwhitneyu. - If a single array was given (x1 only), returns 'NIL'. - The Mann-Whitney U-test is a nonparametric unpaired test of the null - hypothesis that x1 and x2 are from the same distribution. - See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.mannwhitneyu.html + `pct_ci_low,pct_ci_high`:floats + The upper and lower bounds of the confidence interval as computed by taking the percentage bounds. + `pct_low_high_indices`:array + An array with the indices in `stat_array` corresponding to the percentage confidence interval bounds. + `bca_ci_low, bca_ci_high`: floats + The upper and lower bounds of the bias-corrected and accelerated(BCa) confidence interval. See Efron 1977. + `bca_low_high_indices`: array + An array with the indices in `stat_array` corresponding to the BCa confidence interval bounds. + `pvalue_1samp_ttest`: float + P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were passed (x1 and x2), returns 'NIL'. See + `pvalue_2samp_ind_ttest`: float + P-value obtained from scipy.stats.ttest_ind. If a single array was given (x1 only), or if `paired` is not None, returns 'NIL'. See + `pvalue_2samp_related_ttest`: float + P-value obtained from scipy.stats.ttest_rel. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. See + `pvalue_wilcoxon`: float + P-value obtained from scipy.stats.wilcoxon. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. The Wilcoxons signed-rank test is a nonparametric paired test of the null hypothesis that the related samples x1 and x2 are from the same distribution. See + `pvalue_mann_whitney`: float + Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns 'NIL'. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See ''' - def __init__(self, x1, x2=None, - paired=False, - statfunction=None, - smoothboot=False, - alpha_level=0.05, - reps=5000): + def __init__(self, + x1:np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. + x2:np.array=None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. + paired:bool=False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control). + statfunction:callable=np.mean,#The summary statistic called on data. + smoothboot:bool=False,#Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate). + alpha_level:float=0.05,#Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. + reps:int=5000 # Number of bootstrap iterations to perform. + ): import numpy as np import pandas as pd @@ -155,7 +94,7 @@ def __init__(self, x1, x2=None, if len(x1) != len(x2): raise ValueError('x1 and x2 are not the same length.') - if (x2 is None) or (paired is True) : + if (x2 is None) or (paired is not None) : if x2 is None: tx = x1 @@ -165,7 +104,7 @@ def __init__(self, x1, x2=None, ttest_2_paired = 'NIL' wilcoxonresult = 'NIL' - elif paired is True: + elif paired is not None: diff = True tx = x2 - x1 ttest_single = 'NIL' @@ -188,7 +127,7 @@ def __init__(self, x1, x2=None, pct_low_high = np.nan_to_num(pct_low_high).astype('int') - elif x2 is not None and paired is False: + elif x2 is not None and paired is None: diff = True x2 = pd.Series(x2).dropna() # Generate statarrays for both arrays. @@ -268,7 +207,7 @@ def __repr__(self): else: stat = self.statistic - diff_types = {True: 'paired', False: 'unpaired'} + diff_types = {'sequential': 'paired', 'baseline': 'paired', None: 'unpaired'} if self.is_difference: a = 'The {} {} difference is {}.'.format(diff_types[self.is_paired], stat, self.summary) @@ -278,6 +217,7 @@ def __repr__(self): b = '[{} CI: {}, {}]'.format(self.ci, self.bca_ci_low, self.bca_ci_high) return '\n'.join([a, b]) +# %% ../nbs/API/bootstrap.ipynb 5 def jackknife_indexes(data): # Taken without modification from scikits.bootstrap package. """ diff --git a/dabest/_classes.py b/dabest/_classes.py index 8329669a..318832bc 100644 --- a/dabest/_classes.py +++ b/dabest/_classes.py @@ -1,16 +1,24 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/class.ipynb. +# %% auto 0 +__all__ = ['Dabest', 'DeltaDelta', 'MiniMetaDelta', 'TwoGroupsEffectSize', 'EffectSizeDataFrame', 'PermutationTest'] + +# %% ../nbs/API/class.ipynb 4 +import numpy as np +from scipy.stats import norm +import pandas as pd +from scipy.stats import randint + +# %% ../nbs/API/class.ipynb 6 class Dabest(object): """ Class for estimation statistics and plots. """ - def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, - random_seed): + def __init__(self, data, idx, x, y, paired, id_col, ci, + resamples, random_seed, proportional, delta2, + experiment, experiment_label, x1_level, mini_meta): """ Parses and stores pandas DataFrames in preparation for estimation @@ -23,13 +31,16 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, import pandas as pd import seaborn as sns - self.__ci = ci - self.__data = data - self.__idx = idx - self.__id_col = id_col - self.__is_paired = paired - self.__resamples = resamples - self.__random_seed = random_seed + self.__delta2 = delta2 + self.__experiment = experiment + self.__ci = ci + self.__data = data + self.__id_col = id_col + self.__is_paired = paired + self.__resamples = resamples + self.__random_seed = random_seed + self.__proportional = proportional + self.__mini_meta = mini_meta # Make a copy of the data, so we don't make alterations to it. data_in = data.copy() @@ -37,6 +48,131 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, # data_in_index_name = data_in.index.name + # Check if it is a valid mini_meta case + if mini_meta is True: + + # Only mini_meta calculation but not proportional and delta-delta function + if proportional is True: + err0 = '`proportional` and `mini_meta` cannot be True at the same time.' + raise ValueError(err0) + elif delta2 is True: + err0 = '`delta` and `mini_meta` cannot be True at the same time.' + raise ValueError(err0) + + # Check if the columns stated are valid + if all([isinstance(i, str) for i in idx]): + if len(pd.unique([t for t in idx]).tolist())!=2: + err0 = '`mini_meta` is True, but `idx` ({})'.format(idx) + err1 = 'does not contain exactly 2 columns.' + raise ValueError(err0 + err1) + elif all([isinstance(i, (tuple, list)) for i in idx]): + all_idx_lengths = [len(t) for t in idx] + if (np.array(all_idx_lengths) != 2).any(): + err1 = "`mini_meta` is True, but some idx " + err2 = "in {} does not consist only of two groups.".format(idx) + raise ValueError(err1 + err2) + + + + # Check if this is a 2x2 ANOVA case and x & y are valid columns + # Create experiment_label and x1_level + if delta2 is True: + if proportional is True: + err0 = '`proportional` and `delta` cannot be True at the same time.' + raise ValueError(err0) + # idx should not be specified + if idx: + err0 = '`idx` should not be specified when `delta2` is True.'.format(len(x)) + raise ValueError(err0) + + # Check if x is valid + if len(x) != 2: + err0 = '`delta2` is True but the number of variables indicated by `x` is {}.'.format(len(x)) + raise ValueError(err0) + else: + for i in x: + if i not in data_in.columns: + err = '{0} is not a column in `data`. Please check.'.format(i) + raise IndexError(err) + + # Check if y is valid + if not y: + err0 = '`delta2` is True but `y` is not indicated.' + raise ValueError(err0) + elif y not in data_in.columns: + err = '{0} is not a column in `data`. Please check.'.format(y) + raise IndexError(err) + + # Check if experiment is valid + if experiment not in data_in.columns: + err = '{0} is not a column in `data`. Please check.'.format(experiment) + raise IndexError(err) + + # Check if experiment_label is valid and create experiment when needed + if experiment_label: + if len(experiment_label) != 2: + err0 = '`experiment_label` does not have a length of 2.' + raise ValueError(err0) + else: + for i in experiment_label: + if i not in data_in[experiment].unique(): + err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment) + raise IndexError(err) + else: + experiment_label = data_in[experiment].unique() + + # Check if x1_level is valid + if x1_level: + if len(x1_level) != 2: + err0 = '`x1_level` does not have a length of 2.' + raise ValueError(err0) + else: + for i in x1_level: + if i not in data_in[x[0]].unique(): + err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment) + raise IndexError(err) + + else: + x1_level = data_in[x[0]].unique() + elif experiment is not None: + experiment_label = data_in[experiment].unique() + x1_level = data_in[x[0]].unique() + self.__experiment_label = experiment_label + self.__x1_level = x1_level + + + # # Check if idx is specified + # if delta2 is False and not idx: + # err = '`idx` is not a column in `data`. Please check.' + # raise IndexError(err) + + + # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case + if idx is None and x is not None and y is not None: + # add a new column which is a combination of experiment and the first variable + new_col_name = experiment+x[0] + while new_col_name in data_in.columns: + new_col_name += "_" + data_in[new_col_name] = data_in[x[0]].astype(str) + " " + data_in[experiment].astype(str) + + #create idx and record the first and second x variable + idx = [] + for i in list(map(lambda x: str(x), experiment_label)): + temp = [] + for j in list(map(lambda x: str(x), x1_level)): + temp.append(j + " " + i) + idx.append(temp) + + self.__idx = idx + self.__x1 = x[0] + self.__x2 = x[1] + x = new_col_name + else: + self.__idx = idx + self.__x1 = None + self.__x2 = None + + # Determine the kind of estimation plot we need to produce. if all([isinstance(i, str) for i in idx]): @@ -62,18 +198,24 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, raise ValueError(err0 + err1 + err2) else: # mix of string and tuple? - err = 'There seems to be a problem with the idx you' + err = 'There seems to be a problem with the idx you '\ 'entered--{}.'.format(idx) raise ValueError(err) # Having parsed the idx, check if it is a kosher paired plot, # if so stated. - if paired is True: - all_idx_lengths = [len(t) for t in self.__idx] - if (np.array(all_idx_lengths) != 2).any(): - err1 = "`is_paired` is True, but some idx " - err2 = "in {} does not consist only of two groups.".format(idx) - raise ValueError(err1 + err2) + #if paired is True: + # all_idx_lengths = [len(t) for t in self.__idx] + # if (np.array(all_idx_lengths) != 2).any(): + # err1 = "`is_paired` is True, but some idx " + # err2 = "in {} does not consist only of two groups.".format(idx) + # raise ValueError(err1 + err2) + + # Check if there is a typo on paired + if paired is not None: + if paired not in ("baseline", "sequential"): + err = '{} assigned for `paired` is not valid.'.format(paired) + raise ValueError(err) # Determine the type of data: wide or long. @@ -173,9 +315,18 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, # Sanity check that all idxs are paired, if so desired. - if paired is True: + #if paired is True: + # if id_col is None: + # err = "`id_col` must be specified if `is_paired` is set to True." + # raise IndexError(err) + # elif id_col not in plot_data.columns: + # err = "{} is not a column in `data`. ".format(id_col) + # raise IndexError(err) + + # Check if `id_col` is valid + if paired: if id_col is None: - err = "`id_col` must be specified if `is_paired` is set to True." + err = "`id_col` must be specified if `paired` is assigned with a not NoneType value." raise IndexError(err) elif id_col not in plot_data.columns: err = "{} is not a column in `data`. ".format(id_col) @@ -183,7 +334,13 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, EffectSizeDataFrame_kwargs = dict(ci=ci, is_paired=paired, random_seed=random_seed, - resamples=resamples) + resamples=resamples, + proportional=proportional, + delta2=delta2, + experiment_label=self.__experiment_label, + x1_level=self.__x1_level, + x2=self.__x2, + mini_meta = mini_meta) self.__mean_diff = EffectSizeDataFrame(self, "mean_diff", **EffectSizeDataFrame_kwargs) @@ -194,10 +351,13 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples, self.__cohens_d = EffectSizeDataFrame(self, "cohens_d", **EffectSizeDataFrame_kwargs) + self.__cohens_h = EffectSizeDataFrame(self, "cohens_h", + **EffectSizeDataFrame_kwargs) + self.__hedges_g = EffectSizeDataFrame(self, "hedges_g", **EffectSizeDataFrame_kwargs) - if paired is False: + if not paired: self.__cliffs_delta = EffectSizeDataFrame(self, "cliffs_delta", **EffectSizeDataFrame_kwargs) else: @@ -211,14 +371,28 @@ def __repr__(self): from .misc_tools import print_greeting - if self.__is_paired: - es = "Paired e" - else: - es = "E" + # Removed due to the deprecation of is_paired + #if self.__is_paired: + # es = "Paired e" + #else: + # es = "E" greeting_header = print_greeting() - s1 = "{}ffect size(s) ".format(es) + RM_STATUS = {'baseline' : 'for repeated measures against baseline \n', + 'sequential': 'for the sequential design of repeated-measures experiment \n', + 'None' : '' + } + + PAIRED_STATUS = {'baseline' : 'Paired e', + 'sequential' : 'Paired e', + 'None' : 'E' + } + + first_line = {"rm_status" : RM_STATUS[str(self.__is_paired)], + "paired_status": PAIRED_STATUS[str(self.__is_paired)]} + + s1 = "{paired_status}ffect size(s) {rm_status}".format(**first_line) s2 = "with {}% confidence intervals will be computed for:".format(self.__ci) desc_line = s1 + s2 @@ -226,11 +400,23 @@ def __repr__(self): comparisons = [] - for j, current_tuple in enumerate(self.__idx): - control_name = current_tuple[0] + if self.__is_paired == 'sequential': + for j, current_tuple in enumerate(self.__idx): + for ix, test_name in enumerate(current_tuple[1:]): + control_name = current_tuple[ix] + comparisons.append("{} minus {}".format(test_name, control_name)) + else: + for j, current_tuple in enumerate(self.__idx): + control_name = current_tuple[0] + + for ix, test_name in enumerate(current_tuple[1:]): + comparisons.append("{} minus {}".format(test_name, control_name)) - for ix, test_name in enumerate(current_tuple[1:]): - comparisons.append("{} minus {}".format(test_name, control_name)) + if self.__delta2 is True: + comparisons.append("{} minus {} (only for mean difference)".format(self.__experiment_label[1], self.__experiment_label[0])) + + if self.__mini_meta is True: + comparisons.append("weighted delta (only for mean difference)") for j, g in enumerate(comparisons): out.append("{}. {}".format(j+1, g)) @@ -252,29 +438,8 @@ def __repr__(self): @property def mean_diff(self): """ - Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - Example - ------- - >>> from scipy.stats import norm - >>> import pandas as pd - >>> import dabest - >>> control = norm.rvs(loc=0, size=30, random_state=12345) - >>> test = norm.rvs(loc=0.5, size=30, random_state=12345) - >>> my_df = pd.DataFrame({"control": control, - "test": test}) - >>> my_dabest_object = dabest.load(my_df, idx=("control", "test")) - >>> my_dabest_object.mean_diff - - Notes - ----- - This is simply the mean of the control group subtracted from - the mean of the test group. - - .. math:: - \\text{Mean difference} = \\overline{x}_{Test} - \\overline{x}_{Control} - - where :math:`\\overline{x}` is the mean for the group :math:`x`. + Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()` + """ return self.__mean_diff @@ -283,28 +448,7 @@ def mean_diff(self): def median_diff(self): """ Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - Example - ------- - >>> from scipy.stats import norm - >>> import pandas as pd - >>> import dabest - >>> control = norm.rvs(loc=0, size=30, random_state=12345) - >>> test = norm.rvs(loc=0.5, size=30, random_state=12345) - >>> my_df = pd.DataFrame({"control": control, - "test": test}) - >>> my_dabest_object = dabest.load(my_df, idx=("control", "test")) - >>> my_dabest_object.median_diff - - Notes - ----- - This is simply the median of the control group subtracted from - the median of the test group. - - .. math:: - \\text{Median difference} = \\widetilde{x}_{Test} - \\widetilde{x}_{Control} - - where :math:`\\widetilde{x}` is the median for the group :math:`x`. + """ return self.__median_diff @@ -313,98 +457,25 @@ def median_diff(self): def cohens_d(self): """ Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - Example - ------- - >>> from scipy.stats import norm - >>> import pandas as pd - >>> import dabest - >>> control = norm.rvs(loc=0, size=30, random_state=12345) - >>> test = norm.rvs(loc=0.5, size=30, random_state=12345) - >>> my_df = pd.DataFrame({"control": control, - "test": test}) - >>> my_dabest_object = dabest.load(my_df, idx=("control", "test")) - >>> my_dabest_object.cohens_d - - Notes - ----- - Cohen's `d` is simply the mean of the control group subtracted from - the mean of the test group. - - If the comparison(s) are unpaired, Cohen's `d` is computed with the following equation: - - .. math:: - - d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{pooled standard deviation}} - - - For paired comparisons, Cohen's d is given by - - .. math:: - d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{average standard deviation}} - - where :math:`\\overline{x}` is the mean of the respective group of observations, :math:`{Var}_{x}` denotes the variance of that group, - - .. math:: - - \\text{pooled standard deviation} = \\sqrt{ \\frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} } - - and - - .. math:: - - \\text{average standard deviation} = \\sqrt{ \\frac{{Var}_{control} + {Var}_{test}} {2}} - - The sample variance (and standard deviation) uses N-1 degrees of freedoms. - This is an application of `Bessel's correction `_, and yields the unbiased - sample variance. - - References: - https://en.wikipedia.org/wiki/Effect_size#Cohen's_d - https://en.wikipedia.org/wiki/Bessel%27s_correction - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation + """ return self.__cohens_d + @property + def cohens_h(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`. + + """ + return self.__cohens_h + + @property def hedges_g(self): """ Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - - Example - ------- - >>> from scipy.stats import norm - >>> import pandas as pd - >>> import dabest - >>> control = norm.rvs(loc=0, size=30, random_state=12345) - >>> test = norm.rvs(loc=0.5, size=30, random_state=12345) - >>> my_df = pd.DataFrame({"control": control, - "test": test}) - >>> my_dabest_object = dabest.load(my_df, idx=("control", "test")) - >>> my_dabest_object.hedges_g - - Notes - ----- - - Hedges' `g` is :py:attr:`cohens_d` corrected for bias via multiplication with the following correction factor: - - .. math:: - \\frac{ \\Gamma( \\frac{a} {2} )} {\\sqrt{ \\frac{a} {2} } \\times \\Gamma( \\frac{a - 1} {2} )} - - where - - .. math:: - a = {n}_{control} + {n}_{test} - 2 - - and :math:`\\Gamma(x)` is the `Gamma function `_. - - - - References: - https://en.wikipedia.org/wiki/Effect_size#Hedges'_g - https://journals.sagepub.com/doi/10.3102/10769986006002107 + """ return self.__hedges_g @@ -413,63 +484,91 @@ def hedges_g(self): def cliffs_delta(self): """ Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - - Example - ------- - >>> from scipy.stats import norm - >>> import pandas as pd - >>> import dabest - >>> control = norm.rvs(loc=0, size=30, random_state=12345) - >>> test = norm.rvs(loc=0.5, size=30, random_state=12345) - >>> my_df = pd.DataFrame({"control": control, - "test": test}) - >>> my_dabest_object = dabest.load(my_df, idx=("control", "test")) - >>> my_dabest_object.cliffs_delta - - - Notes - ----- - - Cliff's delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample. - - .. math:: - \\text{Cliff's delta} = \\frac{\\#({x}_{test} > {x}_{control}) - \\#({x}_{test} < {x}_{control})} {{n}_{Test} \\times {n}_{Control}} - - - where :math:`\\#` denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample. - - Cliff's delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from. - - References: - https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data - https://psycnet.apa.org/record/1994-08169-001 + """ return self.__cliffs_delta - @property def data(self): """ Returns the pandas DataFrame that was passed to `dabest.load()`. + When `delta2` is True, a new column is added to support the + function. The name of this new column is indicated by `x`. """ return self.__data + @property def idx(self): """ Returns the order of categories that was passed to `dabest.load()`. """ return self.__idx + + + @property + def x1(self): + """ + Returns the first variable declared in x when it is a delta-delta + case; returns None otherwise. + """ + return self.__x1 + + + @property + def x1_level(self): + """ + Returns the levels of first variable declared in x when it is a + delta-delta case; returns None otherwise. + """ + return self.__x1_level + + + @property + def x2(self): + """ + Returns the second variable declared in x when it is a delta-delta + case; returns None otherwise. + """ + return self.__x2 + + + @property + def experiment(self): + """ + Returns the column name of experiment labels that was passed to + `dabest.load()` when it is a delta-delta case; returns None otherwise. + """ + return self.__experiment + + + @property + def experiment_label(self): + """ + Returns the experiment labels in order that was passed to `dabest.load()` + when it is a delta-delta case; returns None otherwise. + """ + return self.__experiment_label + + + @property + def delta2(self): + """ + Returns the boolean parameter indicating if this is a delta-delta + situation. + """ + return self.__delta2 + @property def is_paired(self): """ - Returns True if the dataset was declared as paired to `dabest.load()`. + Returns the type of repeated-measures experiment. """ return self.__is_paired + @property def id_col(self): """ @@ -477,6 +576,7 @@ def id_col(self): """ return self.__id_col + @property def ci(self): """ @@ -484,6 +584,7 @@ def ci(self): """ return self.__ci + @property def resamples(self): """ @@ -491,6 +592,7 @@ def resamples(self): """ return self.__resamples + @property def random_seed(self): """ @@ -501,67 +603,901 @@ def random_seed(self): @property - def x(self): - """ - Returns the x column that was passed to `dabest.load()`, if any. - """ - return self.__x + def x(self): + """ + Returns the x column that was passed to `dabest.load()`, if any. + When `delta2` is True, `x` returns the name of the new column created + for the delta-delta situation. To retrieve the 2 variables passed into + `x` when `delta2` is True, please call `x1` and `x2` instead. + """ + return self.__x + + + @property + def y(self): + """ + Returns the y column that was passed to `dabest.load()`, if any. + """ + return self.__y + + + @property + def _xvar(self): + """ + Returns the xvar in dabest.plot_data. + """ + return self.__xvar + + + @property + def _yvar(self): + """ + Returns the yvar in dabest.plot_data. + """ + return self.__yvar + + + @property + def _plot_data(self): + """ + Returns the pandas DataFrame used to produce the estimation stats/plots. + """ + return self.__plot_data + + + @property + def proportional(self): + """ + Returns the proportional parameter class. + """ + return self.__proportional + + + @property + def mini_meta(self): + """ + Returns the mini_meta boolean parameter. + """ + return self.__mini_meta + + + @property + def _all_plot_groups(self): + """ + Returns the all plot groups, as indicated via the `idx` keyword. + """ + return self.__all_plot_groups + +# %% ../nbs/API/class.ipynb 25 +class DeltaDelta(object): + """ + A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs: + + + $$\Delta_{1} = \overline{X}_{A_{2}, B_{1}} - \overline{X}_{A_{1}, B_{1}}$$ + + $$\Delta_{2} = \overline{X}_{A_{2}, B_{2}} - \overline{X}_{A_{1}, B_{2}}$$ + + + where $\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\Delta$ is the mean difference between two samples. + + A delta-delta value is then calculated as the mean difference between the two primary deltas: + + + $$\Delta_{\Delta} = \Delta_{2} - \Delta_{1}$$ + + """ + + def __init__(self, effectsizedataframe, permutation_count, + ci=95): + + import numpy as np + from numpy import sort as npsort + from numpy import sqrt, isinf, isnan + from ._stats_tools import effsize as es + from ._stats_tools import confint_1group as ci1g + from ._stats_tools import confint_2group_diff as ci2g + + + from string import Template + import warnings + + self.__effsizedf = effectsizedataframe.results + self.__dabest_obj = effectsizedataframe.dabest_obj + self.__ci = ci + self.__resamples = effectsizedataframe.resamples + self.__alpha = ci2g._compute_alpha_from_ci(ci) + self.__permutation_count = permutation_count + self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) + self.__control = self.__dabest_obj.experiment_label[0] + self.__test = self.__dabest_obj.experiment_label[1] + + + # Compute the bootstrap delta-delta and the true dela-delta based on + # the raw data + self.__bootstraps_delta_delta = self.__bootstraps[1] - self.__bootstraps[0] + + self.__difference = self.__effsizedf["difference"][1] - self.__effsizedf["difference"][0] + + + + sorted_delta_delta = npsort(self.__bootstraps_delta_delta) + + self.__bias_correction = ci2g.compute_meandiff_bias_correction( + self.__bootstraps_delta_delta, self.__difference) + + self.__jackknives = np.array(ci1g.compute_1group_jackknife( + self.__bootstraps_delta_delta, + np.mean)) + + self.__acceleration_value = ci2g._calc_accel(self.__jackknives) + + # Compute BCa intervals. + bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( + self.__bias_correction, self.__acceleration_value, + self.__resamples, ci) + + self.__bca_interval_idx = (bca_idx_low, bca_idx_high) + + if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): + self.__bca_low = sorted_delta_delta[bca_idx_low] + self.__bca_high = sorted_delta_delta[bca_idx_high] + + err1 = "The $lim_type limit of the interval" + err2 = "was in the $loc 10 values." + err3 = "The result should be considered unstable." + err_temp = Template(" ".join([err1, err2, err3])) + + if bca_idx_low <= 10: + warnings.warn(err_temp.substitute(lim_type="lower", + loc="bottom"), + stacklevel=1) + + if bca_idx_high >= self.__resamples-9: + warnings.warn(err_temp.substitute(lim_type="upper", + loc="top"), + stacklevel=1) + + else: + err1 = "The $lim_type limit of the BCa interval cannot be computed." + err2 = "It is set to the effect size itself." + err3 = "All bootstrap values were likely all the same." + err_temp = Template(" ".join([err1, err2, err3])) + + if isnan(bca_idx_low): + self.__bca_low = self.__difference + warnings.warn(err_temp.substitute(lim_type="lower"), + stacklevel=0) + + if isnan(bca_idx_high): + self.__bca_high = self.__difference + warnings.warn(err_temp.substitute(lim_type="upper"), + stacklevel=0) + + # Compute percentile intervals. + pct_idx_low = int((self.__alpha/2) * self.__resamples) + pct_idx_high = int((1-(self.__alpha/2)) * self.__resamples) + + self.__pct_interval_idx = (pct_idx_low, pct_idx_high) + self.__pct_low = sorted_delta_delta[pct_idx_low] + self.__pct_high = sorted_delta_delta[pct_idx_high] + + + + def __permutation_test(self): + """ + Perform a permutation test and obtain the permutation p-value + based on the permutation data. + """ + import numpy as np + self.__permutations = np.array(self.__effsizedf["permutations"]) + + THRESHOLD = np.abs(self.__difference) + + self.__permutations_delta_delta = np.array(self.__permutations[1]-self.__permutations[0]) + + count = sum(np.abs(self.__permutations_delta_delta)>THRESHOLD) + self.__pvalue_permutation = count/self.__permutation_count + + + + def __repr__(self, header=True, sigfig=3): + from .__init__ import __version__ + import datetime as dt + import numpy as np + + from .misc_tools import print_greeting + + first_line = {"control" : self.__control, + "test" : self.__test} + + out1 = "The delta-delta between {control} and {test} ".format(**first_line) + + base_string_fmt = "{:." + str(sigfig) + "}" + if "." in str(self.__ci): + ci_width = base_string_fmt.format(self.__ci) + else: + ci_width = str(self.__ci) + + ci_out = {"es" : base_string_fmt.format(self.__difference), + "ci" : ci_width, + "bca_low" : base_string_fmt.format(self.__bca_low), + "bca_high" : base_string_fmt.format(self.__bca_high)} + + out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) + out = out1 + out2 + + if header is True: + out = print_greeting() + "\n" + "\n" + out + + + pval_rounded = base_string_fmt.format(self.pvalue_permutation) + + + p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) + p2 = "calculated for legacy purposes only. " + pvalue = p1 + p2 + + + bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) + bs2 = "the confidence interval is bias-corrected and accelerated." + bs = bs1 + bs2 + + pval_def1 = "Any p-value reported is the probability of observing the " + \ + "effect size (or greater),\nassuming the null hypothesis of " + \ + "zero difference is true." + pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ + "control and test labels were performed." + pval_def = pval_def1 + pval_def2 + + + return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) + + + def to_dict(self): + """ + Returns the attributes of the `DeltaDelta` object as a + dictionary. + """ + # Only get public (user-facing) attributes. + attrs = [a for a in dir(self) + if not a.startswith(("_", "to_dict"))] + out = {} + for a in attrs: + out[a] = getattr(self, a) + return out + + + @property + def ci(self): + """ + Returns the width of the confidence interval, in percent. + """ + return self.__ci + + + @property + def alpha(self): + """ + Returns the significance level of the statistical test as a float + between 0 and 1. + """ + return self.__alpha + + + @property + def bias_correction(self): + return self.__bias_correction + + + @property + def bootstraps(self): + ''' + Return the bootstrapped deltas from all the experiment groups. + ''' + return self.__bootstraps + + + @property + def jackknives(self): + return self.__jackknives + + + @property + def acceleration_value(self): + return self.__acceleration_value + + + @property + def bca_low(self): + """ + The bias-corrected and accelerated confidence interval lower limit. + """ + return self.__bca_low + + + @property + def bca_high(self): + """ + The bias-corrected and accelerated confidence interval upper limit. + """ + return self.__bca_high + + + @property + def bca_interval_idx(self): + return self.__bca_interval_idx + + + @property + def control(self): + ''' + Return the name of the control experiment group. + ''' + return self.__control + + + @property + def test(self): + ''' + Return the name of the test experiment group. + ''' + return self.__test + + + @property + def bootstraps_delta_delta(self): + ''' + Return the delta-delta values calculated from the bootstrapped + deltas. + ''' + return self.__bootstraps_delta_delta + + + @property + def difference(self): + ''' + Return the delta-delta value calculated based on the raw data. + ''' + return self.__difference + + + @property + def pct_interval_idx (self): + return self.__pct_interval_idx + + + @property + def pct_low(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_low + + + @property + def pct_high(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_high + + + @property + def pvalue_permutation(self): + try: + return self.__pvalue_permutation + except AttributeError: + self.__permutation_test() + return self.__pvalue_permutation + + + @property + def permutation_count(self): + """ + The number of permuations taken. + """ + return self.__permutation_count + + + @property + def permutations(self): + ''' + Return the mean differences of permutations obtained during + the permutation test for each experiment group. + ''' + try: + return self.__permutations + except AttributeError: + self.__permutation_test() + return self.__permutations + + + @property + def permutations_delta_delta(self): + ''' + Return the delta-delta values of permutations obtained + during the permutation test. + ''' + try: + return self.__permutations_delta_delta + except AttributeError: + self.__permutation_test() + return self.__permutations_delta_delta + + + +# %% ../nbs/API/class.ipynb 29 +class MiniMetaDelta(object): + """ + A class to compute and store the weighted delta. + A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``. + + """ + + def __init__(self, effectsizedataframe, permutation_count, + ci=95): + + import numpy as np + from numpy import sort as npsort + from numpy import sqrt, isinf, isnan + from ._stats_tools import effsize as es + from ._stats_tools import confint_1group as ci1g + from ._stats_tools import confint_2group_diff as ci2g + + + from string import Template + import warnings + + self.__effsizedf = effectsizedataframe.results + self.__dabest_obj = effectsizedataframe.dabest_obj + self.__ci = ci + self.__resamples = effectsizedataframe.resamples + self.__alpha = ci2g._compute_alpha_from_ci(ci) + self.__permutation_count = permutation_count + self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) + self.__control = np.array(self.__effsizedf["control"]) + self.__test = np.array(self.__effsizedf["test"]) + self.__control_N = np.array(self.__effsizedf["control_N"]) + self.__test_N = np.array(self.__effsizedf["test_N"]) + + + idx = self.__dabest_obj.idx + dat = self.__dabest_obj._plot_data + xvar = self.__dabest_obj._xvar + yvar = self.__dabest_obj._yvar + + # compute the variances of each control group and each test group + control_var=[] + test_var=[] + for j, current_tuple in enumerate(idx): + cname = current_tuple[0] + control = dat[dat[xvar] == cname][yvar].copy() + control_var.append(np.var(control, ddof=1)) + + tname = current_tuple[1] + test = dat[dat[xvar] == tname][yvar].copy() + test_var.append(np.var(test, ddof=1)) + self.__control_var = np.array(control_var) + self.__test_var = np.array(test_var) + + # Compute pooled group variances for each pair of experiment groups + # based on the raw data + self.__group_var = ci2g.calculate_group_var(self.__control_var, + self.__control_N, + self.__test_var, + self.__test_N) + + # Compute the weighted average mean differences of the bootstrap data + # using the pooled group variances of the raw data as the inverse of + # weights + self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta( + self.__group_var, + self.__bootstraps, + self.__resamples) + + # Compute the weighted average mean difference based on the raw data + self.__difference = es.weighted_delta(self.__effsizedf["difference"], + self.__group_var) + + sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta) + + + self.__bias_correction = ci2g.compute_meandiff_bias_correction( + self.__bootstraps_weighted_delta, self.__difference) + + self.__jackknives = np.array(ci1g.compute_1group_jackknife( + self.__bootstraps_weighted_delta, + np.mean)) + + self.__acceleration_value = ci2g._calc_accel(self.__jackknives) + + # Compute BCa intervals. + bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( + self.__bias_correction, self.__acceleration_value, + self.__resamples, ci) + + self.__bca_interval_idx = (bca_idx_low, bca_idx_high) + + if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): + self.__bca_low = sorted_weighted_deltas[bca_idx_low] + self.__bca_high = sorted_weighted_deltas[bca_idx_high] + + err1 = "The $lim_type limit of the interval" + err2 = "was in the $loc 10 values." + err3 = "The result should be considered unstable." + err_temp = Template(" ".join([err1, err2, err3])) + + if bca_idx_low <= 10: + warnings.warn(err_temp.substitute(lim_type="lower", + loc="bottom"), + stacklevel=1) + + if bca_idx_high >= self.__resamples-9: + warnings.warn(err_temp.substitute(lim_type="upper", + loc="top"), + stacklevel=1) + + else: + err1 = "The $lim_type limit of the BCa interval cannot be computed." + err2 = "It is set to the effect size itself." + err3 = "All bootstrap values were likely all the same." + err_temp = Template(" ".join([err1, err2, err3])) + + if isnan(bca_idx_low): + self.__bca_low = self.__difference + warnings.warn(err_temp.substitute(lim_type="lower"), + stacklevel=0) + + if isnan(bca_idx_high): + self.__bca_high = self.__difference + warnings.warn(err_temp.substitute(lim_type="upper"), + stacklevel=0) + + # Compute percentile intervals. + pct_idx_low = int((self.__alpha/2) * self.__resamples) + pct_idx_high = int((1-(self.__alpha/2)) * self.__resamples) + + self.__pct_interval_idx = (pct_idx_low, pct_idx_high) + self.__pct_low = sorted_weighted_deltas[pct_idx_low] + self.__pct_high = sorted_weighted_deltas[pct_idx_high] + + + + def __permutation_test(self): + """ + Perform a permutation test and obtain the permutation p-value + based on the permutation data. + """ + import numpy as np + self.__permutations = np.array(self.__effsizedf["permutations"]) + self.__permutations_var = np.array(self.__effsizedf["permutations_var"]) + + THRESHOLD = np.abs(self.__difference) + + all_num = [] + all_denom = [] + + groups = len(self.__permutations) + for i in range(0, len(self.__permutations[0])): + weight = [1/self.__permutations_var[j][i] for j in range(0, groups)] + all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)])) + all_denom.append(np.sum(weight)) + + output=[] + for i in range(0, len(all_num)): + output.append(all_num[i]/all_denom[i]) + + self.__permutations_weighted_delta = np.array(output) + + count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD) + self.__pvalue_permutation = count/self.__permutation_count + + + + def __repr__(self, header=True, sigfig=3): + from .__init__ import __version__ + import datetime as dt + import numpy as np + + from .misc_tools import print_greeting + + is_paired = self.__dabest_obj.is_paired + + PAIRED_STATUS = {'baseline' : 'paired', + 'sequential' : 'paired', + 'None' : 'unpaired' + } + + first_line = {"paired_status": PAIRED_STATUS[str(is_paired)]} + + + out1 = "The weighted-average {paired_status} mean differences ".format(**first_line) + + base_string_fmt = "{:." + str(sigfig) + "}" + if "." in str(self.__ci): + ci_width = base_string_fmt.format(self.__ci) + else: + ci_width = str(self.__ci) + + ci_out = {"es" : base_string_fmt.format(self.__difference), + "ci" : ci_width, + "bca_low" : base_string_fmt.format(self.__bca_low), + "bca_high" : base_string_fmt.format(self.__bca_high)} + + out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) + out = out1 + out2 + + if header is True: + out = print_greeting() + "\n" + "\n" + out + + + pval_rounded = base_string_fmt.format(self.pvalue_permutation) + + + p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) + p2 = "calculated for legacy purposes only. " + pvalue = p1 + p2 + + + bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) + bs2 = "the confidence interval is bias-corrected and accelerated." + bs = bs1 + bs2 + + pval_def1 = "Any p-value reported is the probability of observing the" + \ + "effect size (or greater),\nassuming the null hypothesis of" + \ + "zero difference is true." + pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ + "control and test labels were performed." + pval_def = pval_def1 + pval_def2 + + + return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) + + + def to_dict(self): + """ + Returns all attributes of the `dabest.MiniMetaDelta` object as a + dictionary. + """ + # Only get public (user-facing) attributes. + attrs = [a for a in dir(self) + if not a.startswith(("_", "to_dict"))] + out = {} + for a in attrs: + out[a] = getattr(self, a) + return out + + + @property + def ci(self): + """ + Returns the width of the confidence interval, in percent. + """ + return self.__ci + + + @property + def alpha(self): + """ + Returns the significance level of the statistical test as a float + between 0 and 1. + """ + return self.__alpha + + + @property + def bias_correction(self): + return self.__bias_correction + + + @property + def bootstraps(self): + ''' + Return the bootstrapped differences from all the experiment groups. + ''' + return self.__bootstraps + + + @property + def jackknives(self): + return self.__jackknives + + + @property + def acceleration_value(self): + return self.__acceleration_value + + + @property + def bca_low(self): + """ + The bias-corrected and accelerated confidence interval lower limit. + """ + return self.__bca_low + + + @property + def bca_high(self): + """ + The bias-corrected and accelerated confidence interval upper limit. + """ + return self.__bca_high + + + @property + def bca_interval_idx(self): + return self.__bca_interval_idx + + + @property + def control(self): + ''' + Return the names of the control groups from all the experiment + groups in order. + ''' + return self.__control + + + @property + def test(self): + ''' + Return the names of the test groups from all the experiment + groups in order. + ''' + return self.__test + + @property + def control_N(self): + ''' + Return the sizes of the control groups from all the experiment + groups in order. + ''' + return self.__control_N + + + @property + def test_N(self): + ''' + Return the sizes of the test groups from all the experiment + groups in order. + ''' + return self.__test_N + + + @property + def control_var(self): + ''' + Return the estimated population variances of the control groups + from all the experiment groups in order. Here the population + variance is estimated from the sample variance. + ''' + return self.__control_var + + + @property + def test_var(self): + ''' + Return the estimated population variances of the control groups + from all the experiment groups in order. Here the population + variance is estimated from the sample variance. + ''' + return self.__test_var + + + @property + def group_var(self): + ''' + Return the pooled group variances of all the experiment groups + in order. + ''' + return self.__group_var + + + @property + def bootstraps_weighted_delta(self): + ''' + Return the weighted-average mean differences calculated from the bootstrapped + deltas and weights across the experiment groups, where the weights are + the inverse of the pooled group variances. + ''' + return self.__bootstraps_weighted_delta + + + @property + def difference(self): + ''' + Return the weighted-average delta calculated from the raw data. + ''' + return self.__difference + + + @property + def pct_interval_idx (self): + return self.__pct_interval_idx + @property - def y(self): + def pct_low(self): """ - Returns the y column that was passed to `dabest.load()`, if any. + The percentile confidence interval lower limit. """ - return self.__y + return self.__pct_low + @property - def _xvar(self): + def pct_high(self): """ - Returns the xvar in dabest.plot_data. + The percentile confidence interval lower limit. """ - return self.__xvar + return self.__pct_high + @property - def _yvar(self): - """ - Returns the yvar in dabest.plot_data. - """ - return self.__yvar + def pvalue_permutation(self): + try: + return self.__pvalue_permutation + except AttributeError: + self.__permutation_test() + return self.__pvalue_permutation + @property - def _plot_data(self): + def permutation_count(self): """ - Returns the pandas DataFrame used to produce the estimation stats/plots. + The number of permuations taken. """ - return self.__plot_data + return self.__permutation_count + @property - def _all_plot_groups(self): - """ - Returns the all plot groups, as indicated via the `idx` keyword. - """ - return self.__all_plot_groups + def permutations(self): + ''' + Return the mean differences of permutations obtained during + the permutation test for each experiment group. + ''' + try: + return self.__permutations + except AttributeError: + self.__permutation_test() + return self.__permutations + @property + def permutations_var(self): + ''' + Return the pooled group variances of permutations obtained during + the permutation test for each experiment group. + ''' + try: + return self.__permutations_var + except AttributeError: + self.__permutation_test() + return self.__permutations_var + + @property + def permutations_weighted_delta(self): + ''' + Return the weighted-average deltas of permutations obtained + during the permutation test. + ''' + try: + return self.__permutations_weighted_delta + except AttributeError: + self.__permutation_test() + return self.__permutations_weighted_delta +# %% ../nbs/API/class.ipynb 34 class TwoGroupsEffectSize(object): """ A class to compute and store the results of bootstrapped mean differences between two groups. - """ - - def __init__(self, control, test, effect_size, - is_paired=False, ci=95, - resamples=5000, - permutation_count=5000, - random_seed=12345): - - """ - Compute the effect size between two groups. + + Compute the effect size between two groups. Parameters ---------- @@ -571,7 +1507,7 @@ def __init__(self, control, test, effect_size, effect_size : string. Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - is_paired : boolean, default False + is_paired : string, default None resamples : int, default 5000 The number of bootstrap resamples to be taken for the calculation of the confidence interval limits. @@ -586,92 +1522,38 @@ def __init__(self, control, test, effect_size, bootstrap resampling. This ensures that the confidence intervals reported are replicable. - Returns ------- - A :py:class:`TwoGroupEffectSize` object. - - difference : float - The effect size of the difference between the control and the test. - - effect_size : string - The type of effect size reported. - - is_paired : boolean - Whether or not the difference is paired (ie. repeated measures). - - ci : float - Returns the width of the confidence interval, in percent. - - alpha : float - Returns the significance level of the statistical test as a float - between 0 and 1. - - resamples : int - The number of resamples performed during the bootstrap procedure. + A :py:class:`TwoGroupEffectSize` object: + `difference` : float + The effect size of the difference between the control and the test. + `effect_size` : string + The type of effect size reported. + `is_paired` : string + The type of repeated-measures experiment. + `ci` : float + Returns the width of the confidence interval, in percent. + `alpha` : float + Returns the significance level of the statistical test as a float between 0 and 1. + `resamples` : int + The number of resamples performed during the bootstrap procedure. + `bootstraps` : numpy ndarray + The generated bootstraps of the effect size. + `random_seed` : int + The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned. + `bca_low, bca_high` : float + The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively. + `pct_low, pct_high` : float + The percentile confidence interval lower limit and upper limits, respectively. + """ + + def __init__(self, control, test, effect_size, + proportional=False, + is_paired=None, ci=95, + resamples=5000, + permutation_count=5000, + random_seed=12345): - bootstraps : nmupy ndarray - The generated bootstraps of the effect size. - - random_seed : int - The number used to initialise the numpy random seed generator, ie. - `seed_value` from `numpy.random.seed(seed_value)` is returned. - - bca_low, bca_high : float - The bias-corrected and accelerated confidence interval lower limit - and upper limits, respectively. - - pct_low, pct_high : float - The percentile confidence interval lower limit and upper limits, - respectively. - - - Examples - -------- - >>> import numpy as np - >>> import scipy as sp - >>> import dabest - >>> np.random.seed(12345) - >>> control = sp.stats.norm.rvs(loc=0, size=30) - >>> test = sp.stats.norm.rvs(loc=0.5, size=30) - >>> effsize = dabest.TwoGroupsEffectSize(control, test, "mean_diff") - >>> effsize - The unpaired mean difference is -0.253 [95%CI -0.782, 0.241] - 5000 bootstrap samples. The confidence interval is bias-corrected - and accelerated. - >>> effsize.to_dict() - {'alpha': 0.05, - 'bca_high': 0.24951887238295106, - 'bca_interval_idx': (125, 4875), - 'bca_low': -0.7801782111071534, - 'bootstraps': array([-1.25579022, -1.20979484, -1.17604415, ..., 0.57700183, - 0.5902485 , 0.61043212]), - 'ci': 95, - 'difference': -0.25315417702752846, - 'effect_size': 'mean difference', - 'is_paired': False, - 'pct_high': 0.24951887238295106, - 'pct_interval_idx': (125, 4875), - 'pct_low': -0.7801782111071534, - 'permutation_count': 5000, - 'pvalue_brunner_munzel': nan, - 'pvalue_kruskal': nan, - 'pvalue_mann_whitney': 0.5201446121616038, - 'pvalue_paired_students_t': nan, - 'pvalue_permutation': 0.3484, - 'pvalue_students_t': 0.34743913903372836, - 'pvalue_welch': 0.3474493875548965, - 'pvalue_wilcoxon': nan, - 'random_seed': 12345, - 'resamples': 5000, - 'statistic_brunner_munzel': nan, - 'statistic_kruskal': nan, - 'statistic_mann_whitney': 494.0, - 'statistic_paired_students_t': nan, - 'statistic_students_t': 0.9472545159069105, - 'statistic_welch': 0.9472545159069105, - 'statistic_wilcoxon': nan} - """ import numpy as np from numpy import array, isnan, isinf @@ -681,17 +1563,19 @@ def __init__(self, control, test, effect_size, import scipy.stats as spstats # import statsmodels.stats.power as power + import statsmodels from string import Template import warnings - - from ._stats_tools import confint_2group_diff as ci2g + from ._stats_tools import effsize as es + from ._stats_tools import confint_2group_diff as ci2g self.__EFFECT_SIZE_DICT = {"mean_diff" : "mean difference", "median_diff" : "median difference", "cohens_d" : "Cohen's d", + "cohens_h" : "Cohen's h", "hedges_g" : "Hedges' g", "cliffs_delta" : "Cliff's delta"} @@ -702,8 +1586,16 @@ def __init__(self, control, test, effect_size, err2 = "is not one of {}".format(kosher_es) raise ValueError(" ".join([err1, err2])) - if effect_size == "cliffs_delta" and is_paired is True: - err1 = "`paired` is True; therefore Cliff's delta is not defined." + if effect_size == "cliffs_delta" and is_paired: + err1 = "`paired` is not None; therefore Cliff's delta is not defined." + raise ValueError(err1) + + if proportional==True and effect_size not in ['mean_diff','cohens_h']: + err1 = "`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined." + raise ValueError(err1) + + if proportional==True and (np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False): + err1 = "`proportional` is True; Only accept binary data consisting of 0 and 1." raise ValueError(err1) # Convert to numpy arrays for speed. @@ -723,10 +1615,9 @@ def __init__(self, control, test, effect_size, self.__ci = ci self.__alpha = ci2g._compute_alpha_from_ci(ci) - self.__difference = es.two_group_difference( control, test, is_paired, effect_size) - + self.__jackknives = ci2g.compute_meandiff_jackknife( control, test, is_paired, effect_size) @@ -735,8 +1626,9 @@ def __init__(self, control, test, effect_size, bootstraps = ci2g.compute_bootstrapped_diff( control, test, is_paired, effect_size, resamples, random_seed) - self.__bootstraps = npsort(bootstraps) + self.__bootstraps = bootstraps + sorted_bootstraps = npsort(self.__bootstraps) # Added in v0.2.6. # Raises a UserWarning if there are any infiinities in the bootstraps. num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)]) @@ -762,8 +1654,8 @@ def __init__(self, control, test, effect_size, self.__bca_interval_idx = (bca_idx_low, bca_idx_high) if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = self.__bootstraps[bca_idx_low] - self.__bca_high = self.__bootstraps[bca_idx_high] + self.__bca_low = sorted_bootstraps[bca_idx_low] + self.__bca_high = sorted_bootstraps[bca_idx_high] err1 = "The $lim_type limit of the interval" err2 = "was in the $loc 10 values." @@ -801,8 +1693,8 @@ def __init__(self, control, test, effect_size, pct_idx_high = int((1-(self.__alpha/2)) * resamples) self.__pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__pct_low = self.__bootstraps[pct_idx_low] - self.__pct_high = self.__bootstraps[pct_idx_high] + self.__pct_low = sorted_bootstraps[pct_idx_low] + self.__pct_high = sorted_bootstraps[pct_idx_high] # Perform statistical tests. @@ -811,30 +1703,39 @@ def __init__(self, control, test, effect_size, is_paired, permutation_count) - if is_paired is True: + if is_paired and proportional is False: # Wilcoxon, a non-parametric version of the paired T-test. wilcoxon = spstats.wilcoxon(control, test) self.__pvalue_wilcoxon = wilcoxon.pvalue self.__statistic_wilcoxon = wilcoxon.statistic - # Introduced in v0.2.8, removed in v0.3.0 for performance issues. -# lqrt_result = lqrt.lqrtest_rel(control, test, -# random_state=random_seed) -# self.__pvalue_paired_lqrt = lqrt_result.pvalue -# self.__statistic_paired_lqrt = lqrt_result.statistic - if effect_size != "median_diff": # Paired Student's t-test. paired_t = spstats.ttest_rel(control, test, nan_policy='omit') self.__pvalue_paired_students_t = paired_t.pvalue self.__statistic_paired_students_t = paired_t.statistic - standardized_es = es.cohens_d(control, test, is_paired=True) + standardized_es = es.cohens_d(control, test, is_paired) # self.__power = power.tt_solve_power(standardized_es, # len(control), # alpha=self.__alpha) + elif is_paired and proportional is True: + # for binary paired data, use McNemar's test + # References: + # https://en.wikipedia.org/wiki/McNemar%27s_test + from statsmodels.stats.contingency_tables import mcnemar + import pandas as pd + df_temp = pd.DataFrame({'control': control, 'test': test}) + x1 = len(df_temp[(df_temp['control'] == 0)&(df_temp['test'] == 0)]) + x2 = len(df_temp[(df_temp['control'] == 0)&(df_temp['test'] == 1)]) + x3 = len(df_temp[(df_temp['control'] == 1)&(df_temp['test'] == 0)]) + x4 = len(df_temp[(df_temp['control'] == 1)&(df_temp['test'] == 1)]) + table = [[x1,x2],[x3,x4]] + _mcnemar = mcnemar(table, exact=True, correction=True) + self.__pvalue_mcnemar = _mcnemar.pvalue + self.__statistic_mcnemar = _mcnemar.statistic elif effect_size == "cliffs_delta": # Let's go with Brunner-Munzel! @@ -880,25 +1781,16 @@ def __init__(self, control, test, effect_size, # in terms of rank (eg. all zeros.) pass - # Introduced in v0.2.8, removed in v0.3.0 for performance issues. -# # Likelihood Q-Ratio test: -# lqrt_equal_var_result = lqrt.lqrtest_ind(control, test, -# random_state=random_seed, -# equal_var=True) - -# self.__pvalue_lqrt_equal_var = lqrt_equal_var_result.pvalue -# self.__statistic_lqrt_equal_var = lqrt_equal_var_result.statistic - -# lqrt_unequal_var_result = lqrt.lqrtest_ind(control, test, -# random_state=random_seed, -# equal_var=False) - -# self.__pvalue_lqrt_unequal_var = lqrt_unequal_var_result.pvalue -# self.__statistic_lqrt_unequal_var = lqrt_unequal_var_result.statistic - standardized_es = es.cohens_d(control, test, is_paired=False) + standardized_es = es.cohens_d(control, test, is_paired = None) + # The Cohen's h calculation is for binary categorical data + try: + self.__proportional_difference = es.cohens_h(control, test) + except ValueError: + # Occur only when the data consists not only 0's and 1's. + pass # self.__power = power.tt_ind_solve_power(standardized_es, # len(control), # alpha=self.__alpha, @@ -924,12 +1816,22 @@ def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3): # "Brunner-Munzel" : "pvalue_brunner_munzel", # "Wilcoxon" : "pvalue_wilcoxon"} - PAIRED_STATUS = {True: 'paired', False: 'unpaired'} - - first_line = {"is_paired": PAIRED_STATUS[self.__is_paired], - "es" : self.__EFFECT_SIZE_DICT[self.__effect_size]} + RM_STATUS = {'baseline' : 'for repeated measures against baseline \n', + 'sequential': 'for the sequential design of repeated-measures experiment \n', + 'None' : '' + } + + PAIRED_STATUS = {'baseline' : 'paired', + 'sequential' : 'paired', + 'None' : 'unpaired' + } + + first_line = {"rm_status" : RM_STATUS[str(self.__is_paired)], + "es" : self.__EFFECT_SIZE_DICT[self.__effect_size], + "paired_status": PAIRED_STATUS[str(self.__is_paired)]} - out1 = "The {is_paired} {es} ".format(**first_line) + + out1 = "The {paired_status} {es} {rm_status}".format(**first_line) base_string_fmt = "{:." + str(sigfig) + "}" if "." in str(self.__ci): @@ -966,14 +1868,17 @@ def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3): # pval_rounded) - pvalue = "The p-value of the two-sided permutation t-test is {}. ".format(pval_rounded) + p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) + p2 = "calculated for legacy purposes only. " + pvalue = p1 + p2 bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) bs2 = "the confidence interval is bias-corrected and accelerated." bs = bs1 + bs2 - pval_def1 = "The p-value(s) reported are the likelihood(s) of observing the " + \ - "effect size(s),\nif the null hypothesis of zero difference is true." + pval_def1 = "Any p-value reported is the probability of observing the" + \ + "effect size (or greater),\nassuming the null hypothesis of" + \ + "zero difference is true." pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ "control and test labels were performed." pval_def = pval_def1 + pval_def2 @@ -1003,8 +1908,6 @@ def to_dict(self): return out - - @property def difference(self): """ @@ -1132,6 +2035,22 @@ def statistic_wilcoxon(self): except AttributeError: return npnan + @property + def pvalue_mcnemar(self): + from numpy import nan as npnan + try: + return self.__pvalue_mcnemar + except AttributeError: + return npnan + + @property + def statistic_mcnemar(self): + from numpy import nan as npnan + try: + return self.__statistic_mcnemar + except AttributeError: + return npnan + @property @@ -1233,90 +2152,44 @@ def pvalue_permutation(self): # @property def permutation_count(self): + """ + The number of permuations taken. + """ return self.__PermutationTest_result.permutation_count - - - # Introduced in v0.2.8, removed in v0.3.0 for performance issues. -# @property -# def pvalue_lqrt_paired(self): -# from numpy import nan as npnan -# try: -# return self.__pvalue_paired_lqrt -# except AttributeError: -# return npnan - - - -# @property -# def statistic_lqrt_paired(self): -# from numpy import nan as npnan -# try: -# return self.__statistic_paired_lqrt -# except AttributeError: -# return npnan - - -# @property -# def pvalue_lqrt_unpaired_equal_variance(self): -# from numpy import nan as npnan -# try: -# return self.__pvalue_lqrt_equal_var -# except AttributeError: -# return npnan - - - -# @property -# def statistic_lqrt_unpaired_equal_variance(self): -# from numpy import nan as npnan -# try: -# return self.__statistic_lqrt_equal_var -# except AttributeError: -# return npnan - - -# @property -# def pvalue_lqrt_unpaired_unequal_variance(self): -# from numpy import nan as npnan -# try: -# return self.__pvalue_lqrt_unequal_var -# except AttributeError: -# return npnan - - - -# @property -# def statistic_lqrt_unpaired_unequal_variance(self): -# from numpy import nan as npnan -# try: -# return self.__statistic_lqrt_unequal_var -# except AttributeError: -# return npnan - - - # @property - # def power(self): - # from numpy import nan as npnan - # try: - # return self.__power - # except AttributeError: - # return npnan + @property + def permutations(self): + return self.__PermutationTest_result.permutations + + @property + def permutations_var(self): + return self.__PermutationTest_result.permutations_var + @property + def proportional_difference(self): + from numpy import nan as npnan + try: + return self.__proportional_difference + except AttributeError: + return npnan +# %% ../nbs/API/class.ipynb 38 class EffectSizeDataFrame(object): """A class that generates and stores the results of bootstrapped effect sizes for several comparisons.""" def __init__(self, dabest, effect_size, - is_paired, ci=95, + is_paired, ci=95, proportional=False, resamples=5000, permutation_count=5000, - random_seed=12345): + random_seed=12345, + x1_level=None, x2=None, + delta2=False, experiment_label=None, + mini_meta=False): """ Parses the data from a Dabest object, enabling plotting and printing capability for the effect size of interest. @@ -1329,6 +2202,12 @@ def __init__(self, dabest, effect_size, self.__resamples = resamples self.__permutation_count = permutation_count self.__random_seed = random_seed + self.__proportional = proportional + self.__x1_level = x1_level + self.__experiment_label = experiment_label + self.__x2 = x2 + self.__delta2 = delta2 + self.__mini_meta = mini_meta def __pre_calc(self): @@ -1344,32 +2223,40 @@ def __pre_calc(self): reprs = [] for j, current_tuple in enumerate(idx): - - cname = current_tuple[0] - control = dat[dat[xvar] == cname][yvar].copy() + if self.__is_paired!="sequential": + cname = current_tuple[0] + control = dat[dat[xvar] == cname][yvar].copy() for ix, tname in enumerate(current_tuple[1:]): + if self.__is_paired == "sequential": + cname = current_tuple[ix] + control = dat[dat[xvar] == cname][yvar].copy() test = dat[dat[xvar] == tname][yvar].copy() result = TwoGroupsEffectSize(control, test, self.__effect_size, + self.__proportional, self.__is_paired, self.__ci, self.__resamples, self.__permutation_count, self.__random_seed) r_dict = result.to_dict() - r_dict["control"] = cname r_dict["test"] = tname r_dict["control_N"] = int(len(control)) r_dict["test_N"] = int(len(test)) - out.append(r_dict) - if j == len(idx)-1 and ix == len(current_tuple)-2: - resamp_count = True - def_pval = True + if self.__delta2 and self.__effect_size == "mean_diff": + resamp_count = False + def_pval = False + elif self.__mini_meta and self.__effect_size == "mean_diff": + resamp_count = False + def_pval = False + else: + resamp_count = True + def_pval = True else: resamp_count = False def_pval = False @@ -1382,12 +2269,6 @@ def __pre_calc(self): reprs.append(text_repr) - varname = get_varname(self.__dabest_obj) - lastline = "To get the results of all valid statistical tests, " +\ - "use `{}.{}.statistical_tests`".format(varname, self.__effect_size) - reprs.append(lastline) - - reprs.insert(0, print_greeting()) self.__for_print = "\n\n".join(reprs) @@ -1402,7 +2283,7 @@ def __pre_calc(self): 'bootstraps', 'resamples', 'random_seed', - 'pvalue_permutation', 'permutation_count', + 'permutations', 'pvalue_permutation', 'permutation_count', 'permutations_var', 'pvalue_welch', 'statistic_welch', @@ -1419,17 +2300,54 @@ def __pre_calc(self): 'pvalue_wilcoxon', 'statistic_wilcoxon', + 'pvalue_mcnemar', + 'statistic_mcnemar', + 'pvalue_paired_students_t', 'statistic_paired_students_t', 'pvalue_kruskal', 'statistic_kruskal', + 'proportional_difference' ] - self.__results = out_.reindex(columns=columns_in_order) self.__results.dropna(axis="columns", how="all", inplace=True) + # Add the is_paired column back when is_paired is None + if self.is_paired is None: + self.__results.insert(5, 'is_paired', self.__results.apply(lambda _: None, axis=1)) + + # Create and compute the delta-delta statistics + if self.__delta2 is True and self.__effect_size == "mean_diff": + self.__delta_delta = DeltaDelta(self, + self.__permutation_count, + self.__ci) + reprs.append(self.__delta_delta.__repr__(header=False)) + elif self.__delta2 is True and self.__effect_size != "mean_diff": + self.__delta_delta = "Delta-delta is not supported for {}.".format(self.__effect_size) + else: + self.__delta_delta = "`delta2` is False; delta-delta is therefore not calculated." + + # Create and compute the weighted average statistics + if self.__mini_meta is True and self.__effect_size == "mean_diff": + self.__mini_meta_delta = MiniMetaDelta(self, + self.__permutation_count, + self.__ci) + reprs.append(self.__mini_meta_delta.__repr__(header=False)) + elif self.__mini_meta is True and self.__effect_size != "mean_diff": + self.__mini_meta_delta = "Weighted delta is not supported for {}.".format(self.__effect_size) + else: + self.__mini_meta_delta = "`mini_meta` is False; weighted delta is therefore not calculated." + + + varname = get_varname(self.__dabest_obj) + lastline = "To get the results of all valid statistical tests, " +\ + "use `{}.{}.statistical_tests`".format(varname, self.__effect_size) + reprs.append(lastline) + reprs.insert(0, print_greeting()) + + self.__for_print = "\n\n".join(reprs) def __repr__(self): @@ -1450,18 +2368,23 @@ def __calc_lqrt(self): dat = db_obj._plot_data xvar = db_obj._xvar yvar = db_obj._yvar + delta2 = self.__delta2 out = [] for j, current_tuple in enumerate(db_obj.idx): - cname = current_tuple[0] - control = dat[dat[xvar] == cname][yvar].copy() + if self.__is_paired != "sequential": + cname = current_tuple[0] + control = dat[dat[xvar] == cname][yvar].copy() for ix, tname in enumerate(current_tuple[1:]): + if self.__is_paired == "sequential": + cname = current_tuple[ix] + control = dat[dat[xvar] == cname][yvar].copy() test = dat[dat[xvar] == tname][yvar].copy() - if self.__is_paired is True: + if self.__is_paired: # Refactored here in v0.3.0 for performance issues. lqrt_result = lqrt.lqrtest_rel(control, test, random_state=rnd_seed) @@ -1492,8 +2415,7 @@ def __calc_lqrt(self): "statistic_lqrt_equal_var" : lqrt_equal_var_result.statistic, "pvalue_lqrt_unequal_var" : lqrt_unequal_var_result.pvalue, "statistic_lqrt_unequal_var" : lqrt_unequal_var_result.statistic, - }) - + }) self.__lqrt_results = pd.DataFrame(out) @@ -1501,14 +2423,22 @@ def plot(self, color_col=None, raw_marker_size=6, es_marker_size=9, - swarm_label=None, contrast_label=None, - swarm_ylim=None, contrast_ylim=None, + swarm_label=None, barchart_label=None, contrast_label=None, delta2_label=None, + swarm_ylim=None, barchart_ylim=None, contrast_ylim=None, delta2_ylim=None, custom_palette=None, swarm_desat=0.5, halfviolin_desat=1, halfviolin_alpha=0.8, + face_color = None, + #bar plot + bar_label=None, bar_desat=0.5, bar_width = 0.5,bar_ylim = None, + # error bar of proportion plot + ci=None, ci_type='bca', err_color=None, + float_contrast=True, show_pairs=True, + show_delta2=True, + show_mini_meta=True, group_summaries=None, group_summaries_offset=0.1, @@ -1517,11 +2447,14 @@ def plot(self, color_col=None, ax=None, swarmplot_kwargs=None, + barplot_kwargs=None, violinplot_kwargs=None, slopegraph_kwargs=None, + sankey_kwargs=None, reflines_kwargs=None, group_summary_kwargs=None, legend_kwargs=None): + """ Creates an estimation plot for the effect size of interest. @@ -1536,16 +2469,24 @@ def plot(self, color_col=None, es_marker_size : float, default 9 The size (in points) of the effect size points on the difference axes. - swarm_label, contrast_label : strings, default None + swarm_label, contrast_label, delta2_label : strings, default None Set labels for the y-axis of the swarmplot and the contrast plot, respectively. If `swarm_label` is not specified, it defaults to "value", unless a column name was passed to `y`. If `contrast_label` is not specified, it defaults to the effect size - being plotted. - swarm_ylim, contrast_ylim : tuples, default None - The desired y-limits of the raw data (swarmplot) axes and the - difference axes respectively, as a tuple. These will be autoscaled - to sensible values if they are not specified. + being plotted. If `delta2_label` is not specifed, it defaults to + "delta - delta" + swarm_ylim, contrast_ylim, delta2_ylim : tuples, default None + The desired y-limits of the raw data (swarmplot) axes, the + difference axes and the delta-delta axes respectively, as a tuple. + These will be autoscaled to sensible values if they are not + specified. The delta2 axes and contrast axes should have the same + limits for y. When `show_delta2` is True, if both of the `contrast_ylim` + and `delta2_ylim` are not None, then they must be specified with the + same values; when `show_delta2` is True and only one of them is specified, + then the other will automatically be assigned with the same value. + Specifying `delta2_ylim` does not have any effect when `show_delta2` is + False. custom_palette : dict, list, or matplotlib color palette, default None This keyword accepts a dictionary with {'group':'color'} pairings, a list of RGB colors, or a specified matplotlib palette. This @@ -1576,6 +2517,9 @@ def plot(self, color_col=None, If the data is paired, whether or not to show the raw data as a swarmplot, or as slopegraph, with a line joining each pair of observations. + show_delta2, show_mini_meta : boolean, default True + If delta-delta or mini-meta delta is calculated, whether or not to + show the delta-delta plot or mini-meta plot. group_summaries : ['mean_sd', 'median_quartiles', 'None'], default None. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a @@ -1607,6 +2551,12 @@ def plot(self, color_col=None, accepted by matplotlib `plot()` function here, as a dict. If None, the following keywords are passed to plot() : {'linewidth':1, 'alpha':0.5}. + sankey_kwargs: dict, default None + Whis will change the appearance of the sankey diagram used to depict + paired proportional data when `show_pairs=True` and `proportional=True`. + Pass any keyword arguments accepted by plot_tools.sankeydiag() function + here, as a dict. If None, the following keywords are passed to sankey diagram: + {"width": 0.5, "align": "center", "alpha": 0.4, "bar_width": 0.1, "rightColor": False} reflines_kwargs : dict, default None This will change the appearance of the zero reference lines. Pass any keyword arguments accepted by the matplotlib Axes `hlines` @@ -1637,48 +2587,6 @@ def plot(self, color_col=None, See the last example below. - Examples - -------- - Create a Gardner-Altman estimation plot for the mean difference. - - >>> my_data = dabest.load(df, idx=("Control 1", "Test 1")) - >>> fig1 = my_data.mean_diff.plot() - - Create a Gardner-Altman plot for the Hedges' g effect size. - - >>> fig2 = my_data.hedges_g.plot() - - Create a Cumming estimation plot for the mean difference. - - >>> fig3 = my_data.mean_diff.plot(float_contrast=True) - - Create a paired Gardner-Altman plot. - - >>> my_data_paired = dabest.load(df, idx=("Control 1", "Test 1"), - ... paired=True) - >>> fig4 = my_data_paired.mean_diff.plot() - - Create a multi-group Cumming plot. - - >>> my_multi_groups = dabest.load(df, idx=(("Control 1", "Test 1"), - ... ("Control 2", "Test 2")) - ... ) - >>> fig5 = my_multi_groups.mean_diff.plot() - - Create a shared control Cumming plot. - - >>> my_shared_control = dabest.load(df, idx=("Control 1", "Test 1", - ... "Test 2", "Test 3") - ... ) - >>> fig6 = my_shared_control.mean_diff.plot() - - Creating estimation plots in individual panels of a figure. - - >>> f, axx = plt.subplots(nrows=2, ncols=2, figsize=(15, 15)) - >>> my_data.mean_diff.plot(ax=axx.flat[0]) - >>> my_data_paired.mean_diff.plot(ax=axx.flat[1]) - >>> my_shared_control.mean_diff.plot(ax=axx.flat[2]) - >>> my_shared_control.mean_diff.plot(ax=axx.flat[3], float_contrast=False) """ @@ -1687,6 +2595,12 @@ def plot(self, color_col=None, if hasattr(self, "results") is False: self.__pre_calc() + if self.__delta2: + color_col = self.__x2 + + # if self.__proportional: + # raw_marker_size = 0.01 + all_kwargs = locals() del all_kwargs["self"] @@ -1695,6 +2609,14 @@ def plot(self, color_col=None, return out + @property + def proportional(self): + """ + Returns the proportional parameter + class. + """ + return self.__proportional + @property def results(self): """Prints all pairwise comparisons nicely.""" @@ -1754,6 +2676,26 @@ def ci(self): """ return self.__ci + @property + def x1_level(self): + return self.__x1_level + + + @property + def x2(self): + return self.__x2 + + + @property + def experiment_label(self): + return self.__experiment_label + + + @property + def delta2(self): + return self.__delta2 + + @property def resamples(self): """ @@ -1780,7 +2722,14 @@ def dabest_obj(self): class. """ return self.__dabest_obj - + + @property + def proportional(self): + """ + Returns the proportional parameter + class. + """ + return self.__proportional @property def lqrt(self): @@ -1795,9 +2744,41 @@ def lqrt(self): self.__calc_lqrt() return self.__lqrt_results - - - + + @property + def mini_meta(self): + """ + Returns the mini_meta boolean parameter. + """ + return self.__mini_meta + + + @property + def mini_meta_delta(self): + """ + Returns the mini_meta results. + """ + try: + return self.__mini_meta_delta + except AttributeError: + self.__pre_calc() + return self.__mini_meta_delta + + + @property + def delta_delta(self): + """ + Returns the mini_meta results. + """ + try: + return self.__delta_delta + except AttributeError: + self.__pre_calc() + return self.__delta_delta + + + +# %% ../nbs/API/class.ipynb 56 class PermutationTest: """ A class to compute and report permutation tests. @@ -1810,64 +2791,38 @@ class PermutationTest: effect_size : string. Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - is_paired : boolean, default False + is_paired : string, default None permutation_count : int, default 10000 The number of permutations (reshuffles) to perform. random_seed : int, default 12345 `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable. - - + Returns ------- - A :py:class:`PermutationTest` object. - - difference : float - The effect size of the difference between the control and the test. - - effect_size : string - The type of effect size reported. - - - Notes - ----- - The basic concept of permutation tests is the same as that behind bootstrapping. - In an "exact" permutation test, all possible resuffles of the control and test - labels are performed, and the proportion of effect sizes that equal or exceed - the observed effect size is computed. This is the probability, under the null - hypothesis of zero difference between test and control groups, of observing the - effect size: the p-value of the Student's t-test. - - Exact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of :math:`20!` or :math:`2.43 \\times {10}^{18}` reshuffles. - Therefore, in practice, "approximate" permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed. - - More information can be found `here `_. + A :py:class:`PermutationTest` object: + `difference`:float + The effect size of the difference between the control and the test. + `effect_size`:string + The type of effect size reported. - Example - ------- - >>> import numpy as np - >>> from scipy.stats import norm - >>> import dabest - >>> control = norm.rvs(loc=0, size=30, random_state=12345) - >>> test = norm.rvs(loc=0.5, size=30, random_state=12345) - >>> perm_test = dabest.PermutationTest(control, test, - ... effect_size="mean_diff", - ... is_paired=False) - >>> perm_test - 5000 permutations were taken. The pvalue is 0.0758. """ - def __init__(self, control, test, - effect_size, is_paired, - permutation_count=5000, - random_seed=12345, + def __init__(self, control:np.array, + test:np.array, # These should be numerical iterables. + effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' + is_paired:str=None, + permutation_count:int=5000, # The number of permutations (reshuffles) to perform. + random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable. **kwargs): import numpy as np from numpy.random import PCG64, RandomState from ._stats_tools.effsize import two_group_difference + from ._stats_tools.confint_2group_diff import calculate_group_var + self.__permutation_count = permutation_count @@ -1892,6 +2847,7 @@ def __init__(self, control, test, THRESHOLD = np.abs(two_group_difference(control, test, is_paired, effect_size)) self.__permutations = [] + self.__permutations_var = [] for i in range(int(permutation_count)): @@ -1918,11 +2874,19 @@ def __init__(self, control, test, es = two_group_difference(control_sample, test_sample, False, effect_size) + var = calculate_group_var(np.var(control_sample, ddof=1), + CONTROL_LEN, + np.var(test_sample, ddof=1), + len(test_sample)) self.__permutations.append(es) + self.__permutations_var.append(var) if np.abs(es) > THRESHOLD: EXTREME_COUNT += 1. + self.__permutations = np.array(self.__permutations) + self.__permutations_var = np.array(self.__permutations_var) + self.pvalue = EXTREME_COUNT / permutation_count @@ -1944,4 +2908,13 @@ def permutations(self): """ The effect sizes of all the permutations in a list. """ - return self.__permutations \ No newline at end of file + return self.__permutations + + + @property + def permutations_var(self): + """ + The experiment group variance of all the permutations in a list. + """ + return self.__permutations_var + diff --git a/dabest/_modidx.py b/dabest/_modidx.py new file mode 100644 index 00000000..88b66c28 --- /dev/null +++ b/dabest/_modidx.py @@ -0,0 +1,75 @@ +# Autogenerated by nbdev + +d = { 'settings': { 'branch': 'master', + 'doc_baseurl': '/DABEST-python', + 'doc_host': 'https://ZHANGROU-99.github.io', + 'git_url': 'https://github.com/ZHANGROU-99/DABEST-python', + 'lib_path': 'dabest'}, + 'syms': { 'dabest._stats_tools.confint_1group': { 'dabest._stats_tools.confint_1group.compute_1group_acceleration': ( 'API/confint_1group.html#compute_1group_acceleration', + 'dabest/_stats_tools/confint_1group.py'), + 'dabest._stats_tools.confint_1group.compute_1group_bias_correction': ( 'API/confint_1group.html#compute_1group_bias_correction', + 'dabest/_stats_tools/confint_1group.py'), + 'dabest._stats_tools.confint_1group.compute_1group_bootstraps': ( 'API/confint_1group.html#compute_1group_bootstraps', + 'dabest/_stats_tools/confint_1group.py'), + 'dabest._stats_tools.confint_1group.compute_1group_jackknife': ( 'API/confint_1group.html#compute_1group_jackknife', + 'dabest/_stats_tools/confint_1group.py'), + 'dabest._stats_tools.confint_1group.create_bootstrap_indexes': ( 'API/confint_1group.html#create_bootstrap_indexes', + 'dabest/_stats_tools/confint_1group.py'), + 'dabest._stats_tools.confint_1group.summary_ci_1group': ( 'API/confint_1group.html#summary_ci_1group', + 'dabest/_stats_tools/confint_1group.py')}, + 'dabest._stats_tools.confint_2group_diff': { 'dabest._stats_tools.confint_2group_diff._calc_accel': ( 'API/confint_2group_diff.html#_calc_accel', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff._compute_alpha_from_ci': ( 'API/confint_2group_diff.html#_compute_alpha_from_ci', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff._compute_quantile': ( 'API/confint_2group_diff.html#_compute_quantile', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff._create_two_group_jackknife_indexes': ( 'API/confint_2group_diff.html#_create_two_group_jackknife_indexes', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.calculate_group_var': ( 'API/confint_2group_diff.html#calculate_group_var', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.calculate_weighted_delta': ( 'API/confint_2group_diff.html#calculate_weighted_delta', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.compute_bootstrapped_diff': ( 'API/confint_2group_diff.html#compute_bootstrapped_diff', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.compute_interval_limits': ( 'API/confint_2group_diff.html#compute_interval_limits', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.compute_meandiff_bias_correction': ( 'API/confint_2group_diff.html#compute_meandiff_bias_correction', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.compute_meandiff_jackknife': ( 'API/confint_2group_diff.html#compute_meandiff_jackknife', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.create_jackknife_indexes': ( 'API/confint_2group_diff.html#create_jackknife_indexes', + 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.create_repeated_indexes': ( 'API/confint_2group_diff.html#create_repeated_indexes', + 'dabest/_stats_tools/confint_2group_diff.py')}, + 'dabest._stats_tools.effsize': { 'dabest._stats_tools.effsize._compute_hedges_correction_factor': ( 'API/effsize.html#_compute_hedges_correction_factor', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize._compute_standardizers': ( 'API/effsize.html#_compute_standardizers', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.cliffs_delta': ( 'API/effsize.html#cliffs_delta', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.cohens_d': ( 'API/effsize.html#cohens_d', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.cohens_h': ( 'API/effsize.html#cohens_h', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.func_difference': ( 'API/effsize.html#func_difference', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.hedges_g': ( 'API/effsize.html#hedges_g', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.two_group_difference': ( 'API/effsize.html#two_group_difference', + 'dabest/_stats_tools/effsize.py'), + 'dabest._stats_tools.effsize.weighted_delta': ( 'API/effsize.html#weighted_delta', + 'dabest/_stats_tools/effsize.py')}, + 'dabest.misc_tools': { 'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'), + 'dabest.misc_tools.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'), + 'dabest.misc_tools.print_greeting': ('API/misc_tools.html#print_greeting', 'dabest/misc_tools.py'), + 'dabest.misc_tools.unpack_and_add': ('API/misc_tools.html#unpack_and_add', 'dabest/misc_tools.py')}, + 'dabest.plot_tools': { 'dabest.plot_tools.check_data_matches_labels': ( 'API/plot_tools.html#check_data_matches_labels', + 'dabest/plot_tools.py'), + 'dabest.plot_tools.error_bar': ('API/plot_tools.html#error_bar', 'dabest/plot_tools.py'), + 'dabest.plot_tools.get_swarm_spans': ('API/plot_tools.html#get_swarm_spans', 'dabest/plot_tools.py'), + 'dabest.plot_tools.halfviolin': ('API/plot_tools.html#halfviolin', 'dabest/plot_tools.py'), + 'dabest.plot_tools.normalize_dict': ('API/plot_tools.html#normalize_dict', 'dabest/plot_tools.py'), + 'dabest.plot_tools.sankeydiag': ('API/plot_tools.html#sankeydiag', 'dabest/plot_tools.py'), + 'dabest.plot_tools.single_sankey': ('API/plot_tools.html#single_sankey', 'dabest/plot_tools.py')}, + 'dabest.plotter': { 'dabest.plotter.EffectSizeDataFramePlotter': ( 'API/plotter.html#effectsizedataframeplotter', + 'dabest/plotter.py')}}} diff --git a/dabest/_stats_tools/confint_1group.py b/dabest/_stats_tools/confint_1group.py index 682a2c59..29d82f74 100644 --- a/dabest/_stats_tools/confint_1group.py +++ b/dabest/_stats_tools/confint_1group.py @@ -1,15 +1,17 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com -""" -A range of functions to compute bootstraps for a single sample. -""" +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_1group.ipynb. +# %% auto 0 +__all__ = ['create_bootstrap_indexes', 'compute_1group_jackknife', 'compute_1group_acceleration', 'compute_1group_bootstraps', + 'compute_1group_bias_correction', 'summary_ci_1group'] + +# %% ../../nbs/API/confint_1group.ipynb 4 +import numpy as np + +# %% ../../nbs/API/confint_1group.ipynb 5 def create_bootstrap_indexes(array, resamples=5000, random_seed=12345): """Given an array-like, returns a generator of bootstrap indexes to be used for resampling. - """i + """ import numpy as np from numpy.random import PCG64, RandomState rng = RandomState(PCG64(random_seed)) @@ -70,51 +72,29 @@ def compute_1group_bias_correction(x, bootstraps, func, *args, **kwargs): -def summary_ci_1group(x, func, resamples=5000, alpha=0.05, random_seed=12345, - sort_bootstraps=True, *args, **kwargs): +def summary_ci_1group(x:np.array,# An numerical iterable. + func, #The function to be applied to x. + resamples:int=5000, #The number of bootstrap resamples to be taken of func(x). + alpha:float=0.05, #Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. + random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable. + sort_bootstraps:bool=True, + *args, **kwargs): """ Given an array-like x, returns func(x), and a bootstrap confidence interval of func(x). - Keywords - -------- - x: array-like - An numerical iterable. - - func: function - The function to be applied to x. - - resamples: int, default 5000 - The number of bootstrap resamples to be taken of func(x). - - alpha: float, default 0.05 - Denotes the likelihood that the confidence interval produced - _does not_ include the true summary statistic. When alpha = 0.05, - a 95% confidence interval is produced. - random_seed: int, default 12345 - `random_seed` is used to seed the random number generator during - bootstrap resampling. This ensures that the confidence intervals - reported are replicable. - - sort_bootstraps: boolean, default True - - - Returns ------- A dictionary with the following five keys: - 'summary': float. + `summary`: float. The outcome of func(x). - - 'func': function. + `func`: function. The function applied to x. - - 'bca_ci_low': float - 'bca_ci_high': float. + `bca_ci_low`: float + `bca_ci_high`: float. The bias-corrected and accelerated confidence interval, for the given alpha. - - 'bootstraps': array. + `bootstraps`: array. The bootstraps used to generate the confidence interval. These will be sorted in ascending order if `sort_bootstraps` was True. @@ -152,3 +132,4 @@ def summary_ci_1group(x, func, resamples=5000, alpha=0.05, random_seed=12345, del B return out + diff --git a/dabest/_stats_tools/confint_2group_diff.py b/dabest/_stats_tools/confint_2group_diff.py index 9282b1be..cc8c0de8 100644 --- a/dabest/_stats_tools/confint_2group_diff.py +++ b/dabest/_stats_tools/confint_2group_diff.py @@ -1,12 +1,14 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com -""" -A range of functions to compute bootstraps for the mean difference -between two groups. -""" +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_2group_diff.ipynb. +# %% auto 0 +__all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'compute_bootstrapped_diff', + 'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var', + 'calculate_weighted_delta'] + +# %% ../../nbs/API/confint_2group_diff.ipynb 4 +import numpy as np + +# %% ../../nbs/API/confint_2group_diff.ipynb 5 def create_jackknife_indexes(data): """ Given an array-like, creates a jackknife bootstrap. @@ -104,31 +106,6 @@ def _calc_accel(jack_dist): return numer / denom - -# def compute_bootstrapped_diff(x0, x1, is_paired, effect_size, -# resamples=5000, random_seed=12345): -# """Bootstraps the effect_size for 2 groups.""" -# from . import effsize as __es -# import numpy as np -# -# np.random.seed(random_seed) -# -# out = np.repeat(np.nan, resamples) -# x0_len = len(x0) -# x1_len = len(x1) -# -# for i in range(int(resamples)): -# x0_boot = np.random.choice(x0, x0_len, replace=True) -# x1_boot = np.random.choice(x1, x1_len, replace=True) -# out[i] = __es.two_group_difference(x0_boot, x1_boot, -# is_paired, effect_size) -# -# # reset seed -# np.random.seed() -# -# return out - - def compute_bootstrapped_diff(x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345): """Bootstraps the effect_size for 2 groups.""" @@ -181,21 +158,13 @@ def compute_bootstrapped_diff(x0, x1, is_paired, effect_size, -def compute_meandiff_bias_correction(bootstraps, effsize): +def compute_meandiff_bias_correction(bootstraps, #An numerical iterable, comprising bootstrap resamples of the effect size. + effsize # The effect size for the original sample. + ): #The bias correction value for the given bootstraps and effect size. """ Computes the bias correction required for the BCa method of confidence interval construction. - Keywords - -------- - bootstraps: array-like - An numerical iterable, comprising bootstrap resamples - of the effect size. - - effsize: numeric - The effect size for the original sample. - - Returns ------- bias: numeric @@ -257,3 +226,20 @@ def compute_interval_limits(bias, acceleration, n_boots, ci=95): low = int(norm.cdf(low) * n_boots) high = int(norm.cdf(high) * n_boots) return low, high + + +def calculate_group_var(control_var, control_N,test_var, test_N): + return control_var/control_N + test_var/test_N + + +def calculate_weighted_delta(group_var, differences, resamples): + ''' + Compute the weighted deltas. + ''' + import numpy as np + + weight = 1/group_var + denom = np.sum(weight) + num = np.sum(weight[i] * differences[i] for i in range(0, len(weight))) + + return num/denom diff --git a/dabest/_stats_tools/effsize.py b/dabest/_stats_tools/effsize.py index 1637085a..ddc8681f 100644 --- a/dabest/_stats_tools/effsize.py +++ b/dabest/_stats_tools/effsize.py @@ -1,43 +1,33 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com -""" -A range of functions to compute various effect sizes. - - two_group_difference - cohens_d - hedges_g - cliffs_delta - func_difference -""" - - -def two_group_difference(control, test, is_paired=False, - effect_size="mean_diff"): +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/effsize.ipynb. + +# %% ../../nbs/API/effsize.ipynb 4 +from __future__ import annotations +import numpy as np + +# %% auto 0 +__all__ = ['two_group_difference', 'func_difference', 'cohens_d', 'cohens_h', 'hedges_g', 'cliffs_delta', 'weighted_delta'] + +# %% ../../nbs/API/effsize.ipynb 5 +def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types. + test:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types. + is_paired=None, #If not None, returns the paired Cohen's d + effect_size:str="mean_diff" # Any one of the following effect sizes: ["mean_diff", "median_diff", "cohens_d", "hedges_g", "cliffs_delta"] + )->float: # The desired effect size. """ Computes the following metrics for control and test: + - Unstandardized mean difference - Standardized mean differences (paired or unpaired) * Cohen's d * Hedges' g - Median difference - Cliff's Delta + - Cohen's h (distance between two proportions) - See the Wikipedia entry here: https://bit.ly/2LzWokf - - Parameters - ---------- - control, test: list, tuple, or ndarray. - Accepts lists, tuples, or numpy ndarrays of numeric types. - - is_paired: boolean, default False. - If True, returns the paired Cohen's d. - - effect_size: string, default "mean_diff" - Any one of the following effect sizes: - ["mean_diff", "median_diff", "cohens_d", "hedges_g", "cliffs_delta"] - + See the Wikipedia entry [here](https://bit.ly/2LzWokf) + + `effect_size`: + mean_diff: This is simply the mean of `control` subtracted from the mean of `test`. @@ -65,51 +55,50 @@ def two_group_difference(control, test, is_paired=False, median_diff: This is the median of `control` subtracted from the median of `test`. - Returns - ------- - float: The desired effect size. """ import numpy as np + import warnings if effect_size == "mean_diff": return func_difference(control, test, np.mean, is_paired) elif effect_size == "median_diff": + mes1 = "Using median as the statistic in bootstrapping may " + \ + "result in a biased estimate and cause problems with " + \ + "BCa confidence intervals. Consider using a different statistic, such as the mean.\n" + mes2 = "When plotting, please consider using percetile confidence intervals " + \ + "by specifying `ci_type='percentile'`. For detailed information, " + \ + "refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n" + warnings.warn(message=mes1+mes2, category=UserWarning) return func_difference(control, test, np.median, is_paired) elif effect_size == "cohens_d": return cohens_d(control, test, is_paired) + elif effect_size == "cohens_h": + return cohens_h(control, test) + elif effect_size == "hedges_g": return hedges_g(control, test, is_paired) elif effect_size == "cliffs_delta": - if is_paired is True: - err1 = "`is_paired` is True; therefore Cliff's delta is not defined." + if is_paired: + err1 = "`is_paired` is not None; therefore Cliff's delta is not defined." raise ValueError(err1) else: return cliffs_delta(control, test) - -def func_difference(control, test, func, is_paired): +# %% ../../nbs/API/effsize.ipynb 6 +def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically discarded. + test:list|tuple|np.ndarray, # NaNs are automatically discarded. + func, # Summary function to apply. + is_paired:str # If not None, computes func(test - control). If None, computes func(test) - func(control). + )->float: """ + Applies func to `control` and `test`, and then returns the difference. - - Keywords: - -------- - control, test: List, tuple, or array. - NaNs are automatically discarded. - - func: summary function to apply. - - is_paired: boolean. - If True, computes func(test - control). - If False, computes func(test) - func(control). - - Returns: - -------- - diff: float. + """ import numpy as np @@ -145,51 +134,49 @@ def func_difference(control, test, func, is_paired): return func(test) - func(control) - -def cohens_d(control, test, is_paired=False): +# %% ../../nbs/API/effsize.ipynb 7 +def cohens_d(control:list|tuple|np.ndarray, + test:list|tuple|np.ndarray, + is_paired:str=None # If not None, the paired Cohen's d is returned. + )->float: """ Computes Cohen's d for test v.s. control. - See https://en.wikipedia.org/wiki/Effect_size#Cohen's_d - - Keywords - -------- - control, test: List, tuple, or array. - - is_paired: boolean, default False - If True, the paired Cohen's d is returned. - - Returns - ------- - d: float. - If is_paired is False, this is equivalent to: - (numpy.mean(test) - numpy.mean(control)) / pooled StDev - - If is_paired is True, returns - (numpy.mean(test) - numpy.mean(control)) / average StDev - - The pooled standard deviation is equal to: - - (n1 - 1) * var(control) + (n2 - 1) * var (test) - sqrt( ---------------------------------------------- ) - (n1 + n2 - 2) - - - The average standard deviation is equal to: - - - var(control) + var(test) - sqrt( ------------------------- ) - 2 - - Notes - ----- - The sample variance (and standard deviation) uses N-1 degrees of freedoms. + See [here](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d) + + If `is_paired` is None, returns: + + $$ + \\frac{\\bar{X}_2 - \\bar{X}_1}{s_{pooled}} + $$ + + where + + $$ + s_{pooled} = \\sqrt{\\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}} + $$ + + If `is_paired` is not None, returns: + + $$ + \\frac{\\bar{X}_2 - \\bar{X}_1}{s_{avg}} + $$ + + where + + $$ + s_{avg} = \\sqrt{\\frac{s_1^2 + s_2^2}{2}} + $$ + + `Notes`: + + - The sample variance (and standard deviation) uses N-1 degrees of freedoms. This is an application of Bessel's correction, and yields the unbiased sample variance. - References: - https://en.wikipedia.org/wiki/Bessel%27s_correction - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation + `References`: + + - https://en.wikipedia.org/wiki/Bessel%27s_correction + - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation """ import numpy as np @@ -225,24 +212,59 @@ def cohens_d(control, test, is_paired=False): return M / divisor +# %% ../../nbs/API/effsize.ipynb 8 +def cohens_h(control:list|tuple|np.ndarray, + test:list|tuple|np.ndarray + )->float: + ''' + Computes Cohen's h for test v.s. control. + See [here](https://en.wikipedia.org/wiki/Cohen%27s_h for reference.) + + `Notes`: + + - Assuming the input data type is binary, i.e. a series of 0s and 1s, + and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: "Smoker", 0: "Non-smoker"} + ''' + + import numpy as np + np.seterr(divide='ignore', invalid='ignore') + import pandas as pd + + # Check whether dataframe contains only 0s and 1s. + if np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False: + raise ValueError("Input data must be binary.") + + # Convert to numpy arrays for speed. + # NaNs are automatically dropped. + # Aligned with cohens_d calculation. + if control.__class__ != np.ndarray: + control = np.array(control) + if test.__class__ != np.ndarray: + test = np.array(test) + control = control[~np.isnan(control)] + test = test[~np.isnan(test)] + + prop_control = sum(control)/len(control) + prop_test = sum(test)/len(test) + + # Arcsine transformation + phi_control = 2 * np.arcsin(np.sqrt(prop_control)) + phi_test = 2 * np.arcsin(np.sqrt(prop_test)) + + return phi_test - phi_control -def hedges_g(control, test, is_paired=False): +# %% ../../nbs/API/effsize.ipynb 9 +def hedges_g(control:list|tuple|np.ndarray, + test:list|tuple|np.ndarray, + is_paired:str=None)->float: """ Computes Hedges' g for for test v.s. control. It first computes Cohen's d, then calulates a correction factor based on the total degress of freedom using the gamma function. - See https://en.wikipedia.org/wiki/Effect_size#Hedges'_g + See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) - Keywords - -------- - control, test: numeric iterables. - These can be lists, tuples, or arrays of numeric types. - - Returns - ------- - g: float. """ import numpy as np @@ -261,21 +283,13 @@ def hedges_g(control, test, is_paired=False): correction_factor = _compute_hedges_correction_factor(len_c, len_t) return correction_factor * d - - -def cliffs_delta(control, test): +# %% ../../nbs/API/effsize.ipynb 10 +def cliffs_delta(control:list|tuple|np.ndarray, + test:list|tuple|np.ndarray + )->float: """ Computes Cliff's delta for 2 samples. - See https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data - - Keywords - -------- - control, test: numeric iterables. - These can be lists, tuples, or arrays of numeric types. - - Returns - ------- - A single numeric float. + See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data) """ import numpy as np from scipy.stats import mannwhitneyu @@ -312,7 +326,7 @@ def cliffs_delta(control, test): return cliffs_delta - +# %% ../../nbs/API/effsize.ipynb 11 def _compute_standardizers(control, test): from numpy import mean, var, sqrt, nan # For calculation of correlation; not currently used. @@ -347,21 +361,18 @@ def _compute_standardizers(control, test): # else: return pooled, average # indent if you implement above code chunk. - - -def _compute_hedges_correction_factor(n1, n2): +# %% ../../nbs/API/effsize.ipynb 12 +def _compute_hedges_correction_factor(n1, + n2 + )->float: """ Computes the bias correction factor for Hedges' g. - See https://en.wikipedia.org/wiki/Effect_size#Hedges'_g - - Returns - ------- - j: float + See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) - References - ---------- - Larry V. Hedges & Ingram Olkin (1985). + `References`: + + - Larry V. Hedges & Ingram Olkin (1985). Statistical Methods for Meta-Analysis. Orlando: Academic Press. ISBN 0-12-336380-2. """ @@ -386,3 +397,14 @@ def _compute_hedges_correction_factor(n1, n2): out = numer / denom return out + +# %% ../../nbs/API/effsize.ipynb 13 +def weighted_delta(difference, group_var): + ''' + Compute the weighted deltas where the weight is the inverse of the + pooled group difference. + ''' + import numpy as np + + weight = np.true_divide(1, group_var) + return np.sum(difference*weight)/np.sum(weight) diff --git a/dabest/misc_tools.py b/dabest/misc_tools.py index af49b71b..4b2617ef 100644 --- a/dabest/misc_tools.py +++ b/dabest/misc_tools.py @@ -1,24 +1,19 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/misc_tools.ipynb. -# CONVENIENCE FUNCTIONS THAT DON'T DIRECTLY DEAL WITH PLOTTING OR -# BOOTSTRAP COMPUTATIONS ARE PLACED HERE. +# %% auto 0 +__all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname'] -def merge_two_dicts(x, y): +# %% ../nbs/API/misc_tools.ipynb 4 +def merge_two_dicts(x:dict, + y:dict + )->dict:#A dictionary containing a union of all keys in both original dicts. """ Given two dicts, merge them into a new dict as a shallow copy. Any overlapping keys in `y` will override the values in `x`. - Taken from https://stackoverflow.com/questions/38987/ - how-to-merge-two-python-dictionaries-in-a-single-expression + Taken from [here](https://stackoverflow.com/questions/38987/ + how-to-merge-two-python-dictionaries-in-a-single-expression) - Parameters: - x, y: dicts - - Returns: - A dictionary containing a union of all keys in both original dicts. """ z = x.copy() z.update(y) @@ -63,3 +58,4 @@ def get_varname(obj): return matching_vars[0] else: return "" + diff --git a/dabest/plot_tools.py b/dabest/plot_tools.py index 377b9eda..a349afdc 100644 --- a/dabest/plot_tools.py +++ b/dabest/plot_tools.py @@ -1,14 +1,21 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com -# A set of convenience functions used for producing plots in `dabest`. +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plot_tools.ipynb. +# %% ../nbs/API/plot_tools.ipynb 2 +from __future__ import annotations -from .misc_tools import merge_two_dicts - +# %% auto 0 +__all__ = ['halfviolin', 'get_swarm_spans', 'error_bar', 'check_data_matches_labels', 'normalize_dict', 'single_sankey', + 'sankeydiag'] +# %% ../nbs/API/plot_tools.ipynb 4 +import pandas as pd +from collections import defaultdict +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +import itertools +# %% ../nbs/API/plot_tools.ipynb 5 def halfviolin(v, half='right', fill_color='k', alpha=1, line_color='k', line_width=0): import numpy as np @@ -34,28 +41,6 @@ def halfviolin(v, half='right', fill_color='k', alpha=1, b.set_linewidth(line_width) - -# def align_yaxis(ax1, v1, ax2, v2): -# """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1""" -# # Taken from -# # http://stackoverflow.com/questions/7630778/ -# # matplotlib-align-origin-of-right-axis-with-specific-left-axis-value -# _, y1 = ax1.transData.transform((0, v1)) -# _, y2 = ax2.transData.transform((0, v2)) -# inv = ax2.transData.inverted() -# _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2)) -# miny, maxy = ax2.get_ylim() -# ax2.set_ylim(miny+dy, maxy+dy) -# -# -# -# def rotate_ticks(axes, angle=45, alignment='right'): -# for tick in axes.get_xticklabels(): -# tick.set_rotation(angle) -# tick.set_horizontalalignment(alignment) - - - def get_swarm_spans(coll): """ Given a matplotlib Collection, will obtain the x and y spans @@ -68,52 +53,24 @@ def get_swarm_spans(coll): except ValueError: return None - - -def gapped_lines(data, x, y, type='mean_sd', offset=0.2, ax=None, - line_color="black", gap_width_percent=1, - **kwargs): +def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format. + x:str, #x column to be plotted. + y:str, # y column to be plotted. + type:str='mean_sd', # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead. + offset:float=0.2, #Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets. + ax=None, #If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used. + line_color="black", gap_width_percent=1, + pos:list=[0, 1],#The positions of the error bars for the sankey_error_bar method. + method:str='gapped_lines', #The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'. + **kwargs:dict + ): ''' - Convenience function to plot the standard devations as vertical - errorbars. The mean is a gap defined by negative space. - - This style is inspired by Edward Tufte's redesign of the boxplot. - See The Visual Display of Quantitative Information (1983), pp.128-130. + Function to plot the standard deviations as vertical errorbars. + The mean is a gap defined by negative space. - Keywords - -------- - data: pandas DataFrame. - This DataFrame should be in 'long' format. + This function combines the functionality of gapped_lines(), + proportional_error_bar(), and sankey_error_bar(). - x, y: string. - x and y columns to be plotted. - - type: ['mean_sd', 'median_quartiles'], default 'mean_sd' - Plots the summary statistics for each group. If 'mean_sd', then the - mean and standard deviation of each group is plotted as a gapped line. - If 'median_quantiles', then the median and 25th and 75th percentiles of - each group is plotted instead. - - offset: float (default 0.3) or iterable. - Give a single float (that will be used as the x-offset of all - gapped lines), or an iterable containing the list of x-offsets. - - line_color: string (matplotlib color, default "black") or iterable of - matplotlib colors. - - The color of the vertical line indicating the stadard deviations. - - gap_width_percent: float, default 5 - The width of the gap in the line (indicating the central measure), - expressed as a percentage of the y-span of the axes. - - ax: matplotlib Axes object, default None - If a matplotlib Axes object is specified, the gapped lines will be - plotted in order on this axes. If None, the current axes (plt.gca()) - is used. - - kwargs: dict, default None - Dictionary with kwargs passed to matplotlib.lines.Line2D ''' import numpy as np import pandas as pd @@ -122,12 +79,14 @@ def gapped_lines(data, x, y, type='mean_sd', offset=0.2, ax=None, if gap_width_percent < 0 or gap_width_percent > 100: raise ValueError("`gap_width_percent` must be between 0 and 100.") + if method not in ['gapped_lines', 'proportional_error_bar', 'sankey_error_bar']: + raise ValueError("Invalid `method`. Must be one of 'gapped_lines', 'proportional_error_bar', or 'sankey_error_bar'.") if ax is None: ax = plt.gca() ax_ylims = ax.get_ylim() ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0]) - gap_width = ax_yspan * gap_width_percent/100 + gap_width = ax_yspan * gap_width_percent / 100 keys = kwargs.keys() if 'clip_on' not in keys: @@ -139,33 +98,32 @@ def gapped_lines(data, x, y, type='mean_sd', offset=0.2, ax=None, if 'lw' not in keys: kwargs['lw'] = 2. - # # Grab the order in which the groups appear. - # group_order = pd.unique(data[x]) - - # Grab the order in which the groups appear, - # depending on whether the x-column is categorical. if isinstance(data[x].dtype, pd.CategoricalDtype): group_order = pd.unique(data[x]).categories else: group_order = pd.unique(data[x]) - means = data.groupby(x)[y].mean().reindex(index=group_order) - sd = data.groupby(x)[y].std().reindex(index=group_order) + means = data.groupby(x)[y].mean().reindex(index=group_order) + + if method in ['proportional_error_bar', 'sankey_error_bar']: + g = lambda x: np.sqrt((np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x))) + sd = data.groupby(x)[y].apply(g) + else: + sd = data.groupby(x)[y].std().reindex(index=group_order) + lower_sd = means - sd upper_sd = means + sd - if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any(): kwargs['clip_on'] = True - medians = data.groupby(x)[y].median().reindex(index=group_order) - quantiles = data.groupby(x)[y].quantile([0.25, 0.75])\ - .unstack()\ - .reindex(index=group_order) + medians = data.groupby(x)[y].median().reindex(index=group_order) + quantiles = data.groupby(x)[y].quantile([0.25, 0.75]) \ + .unstack() \ + .reindex(index=group_order) lower_quartiles = quantiles[0.25] upper_quartiles = quantiles[0.75] - if type == 'mean_sd': central_measures = means lows = lower_sd @@ -175,7 +133,6 @@ def gapped_lines(data, x, y, type='mean_sd', offset=0.2, ax=None, lows = lower_quartiles highs = upper_quartiles - n_groups = len(central_measures) if isinstance(line_color, str): @@ -201,30 +158,374 @@ def gapped_lines(data, x, y, type='mean_sd', offset=0.2, ax=None, kwargs['zorder'] = kwargs['zorder'] for xpos, central_measure in enumerate(central_measures): - # add lower vertical span line. - kwargs['color'] = custom_palette[xpos] - _xpos = xpos + offset[xpos] - # add lower vertical span line. + if method == 'sankey_error_bar': + _xpos = pos[xpos] + offset[xpos] + else: + _xpos = xpos + offset[xpos] + low = lows[xpos] low_to_mean = mlines.Line2D([_xpos, _xpos], - [low, central_measure-gap_width], - **kwargs) + [low, central_measure - gap_width], + **kwargs) ax.add_line(low_to_mean) - # add upper vertical span line. high = highs[xpos] mean_to_high = mlines.Line2D([_xpos, _xpos], - [central_measure+gap_width, high], - **kwargs) + [central_measure + gap_width, high], + **kwargs) ax.add_line(mean_to_high) - # # add horzontal central measure line. - # kwargs['zorder'] = 6 - # kwargs['color'] = gap_color - # kwargs['lw'] = kwargs['lw'] * 1.5 - # line_xpos = xpos + offset[xpos] - # mean_line = mlines.Line2D([line_xpos-0.015, line_xpos+0.015], - # [central_measure, central_measure], **kwargs) - # ax.add_line(mean_line) +def check_data_matches_labels(labels,#list of input labels + data, #Pandas Series of input data + side:str # 'left' or 'right' on the sankey diagram + ): + ''' + Function to check that the labels and data match in the sankey diagram. + And enforce labels and data to be lists. + Raises an exception if the labels and data do not match. + ''' + if len(labels > 0): + if isinstance(data, list): + data = set(data) + if isinstance(data, pd.Series): + data = set(data.unique()) + if isinstance(labels, list): + labels = set(labels) + if labels != data: + msg = "\n" + if len(labels) <= 20: + msg = "Labels: " + ",".join(labels) + "\n" + if len(data) < 20: + msg += "Data: " + ",".join(data) + raise Exception('{0} labels and data do not match.{1}'.format(side, msg)) + +def normalize_dict(nested_dict, target): + val = {} + for key in nested_dict.keys(): + val[key] = np.sum([nested_dict[sub_key][key] for sub_key in nested_dict.keys()]) + + for key, value in nested_dict.items(): + if isinstance(value, dict): + for subkey in value.keys(): + value[subkey] = value[subkey] * target[subkey]['right']/val[subkey] + return nested_dict + +def single_sankey(left:np.array,# data on the left of the diagram + right:np.array, # data on the right of the diagram, len(left) == len(right) + xpos:float=0, # the starting point on the x-axis + leftWeight:np.array=None, #weights for the left labels, if None, all weights are 1 + rightWeight:np.array=None, #weights for the right labels, if None, all weights are corresponding leftWeight + colorDict:dict=None, #input format: {'label': 'color'} + leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels. + rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels. + ax=None, #matplotlib axes to be drawn on + width=0.5, + alpha=0.65, + bar_width=0.2, + rightColor:bool=False, #if True, each strip of the diagram will be colored according to the corresponding left labels + align:bool='center'# if 'center', the diagram will be centered on each xtick, if 'edge', the diagram will be aligned with the left edge of each xtick + ): + + ''' + Make a single Sankey diagram showing proportion flow from left to right + Original code from: https://github.com/anazalea/pySankey + Changes are added to normalize each diagram's height to be 1 + + ''' + + # Initiating values + if ax is None: + ax = plt.gca() + + if leftWeight is None: + leftWeight = [] + if rightWeight is None: + rightWeight = [] + if leftLabels is None: + leftLabels = [] + if rightLabels is None: + rightLabels = [] + # Check weights + if len(leftWeight) == 0: + leftWeight = np.ones(len(left)) + if len(rightWeight) == 0: + rightWeight = leftWeight + + # Create Dataframe + if isinstance(left, pd.Series): + left.reset_index(drop=True, inplace=True) + if isinstance(right, pd.Series): + right.reset_index(drop=True, inplace=True) + dataFrame = pd.DataFrame({'left': left, 'right': right, 'leftWeight': leftWeight, + 'rightWeight': rightWeight}, index=range(len(left))) + + if dataFrame[['left', 'right']].isnull().any(axis=None): + raise Exception('Sankey graph does not support null values.') + + # Identify all labels that appear 'left' or 'right' + allLabels = pd.Series(np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1]).unique() + + # Identify left labels + if len(leftLabels) == 0: + leftLabels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique() + else: + check_data_matches_labels(leftLabels, dataFrame['left'], 'left') + + # Identify right labels + if len(rightLabels) == 0: + rightLabels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique() + else: + check_data_matches_labels(leftLabels, dataFrame['right'], 'right') + + # If no colorDict given, make one + if colorDict is None: + colorDict = {} + palette = "hls" + colorPalette = sns.color_palette(palette, len(allLabels)) + for i, label in enumerate(allLabels): + colorDict[label] = colorPalette[i] + fail_color = {0:"grey"} + colorDict.update(fail_color) + else: + missing = [label for label in allLabels if label not in colorDict.keys()] + if missing: + msg = "The palette parameter is missing values for the following labels : " + msg += '{}'.format(', '.join(missing)) + raise ValueError(msg) + + if align not in ("center", "edge"): + err = '{} assigned for `align` is not valid.'.format(align) + raise ValueError(err) + if align == "center": + try: + leftpos = xpos - width / 2 + except TypeError as e: + raise TypeError(f'the dtypes of parameters x ({xpos.dtype}) ' + f'and width ({width.dtype}) ' + f'are incompatible') from e + else: + leftpos = xpos + + # Combine left and right arrays to have a pandas.DataFrame in the 'long' format + left_series = pd.Series(left, name='values').to_frame().assign(groups='left') + right_series = pd.Series(right, name='values').to_frame().assign(groups='right') + concatenated_df = pd.concat([left_series, right_series], ignore_index=True) + + # Determine positions of left label patches and total widths + # We also want the height of the graph to be 1 + leftWidths_norm = defaultdict() + for i, leftLabel in enumerate(leftLabels): + myD = {} + myD['left'] = (dataFrame[dataFrame.left == leftLabel].leftWeight.sum()/ \ + dataFrame.leftWeight.sum())*(1-(len(leftLabels)-1)*0.02) + if i == 0: + myD['bottom'] = 0 + myD['top'] = myD['left'] + else: + myD['bottom'] = leftWidths_norm[leftLabels[i - 1]]['top'] + 0.02 + myD['top'] = myD['bottom'] + myD['left'] + topEdge = myD['top'] + leftWidths_norm[leftLabel] = myD + + # Determine positions of right label patches and total widths + rightWidths_norm = defaultdict() + for i, rightLabel in enumerate(rightLabels): + myD = {} + myD['right'] = (dataFrame[dataFrame.right == rightLabel].rightWeight.sum()/ \ + dataFrame.rightWeight.sum())*(1-(len(leftLabels)-1)*0.02) + if i == 0: + myD['bottom'] = 0 + myD['top'] = myD['right'] + else: + myD['bottom'] = rightWidths_norm[rightLabels[i - 1]]['top'] + 0.02 + myD['top'] = myD['bottom'] + myD['right'] + topEdge = myD['top'] + rightWidths_norm[rightLabel] = myD + + # Total width of the graph + xMax = width + + # Determine widths of individual strips, all widths are normalized to 1 + ns_l = defaultdict() + ns_r = defaultdict() + ns_l_norm = defaultdict() + ns_r_norm = defaultdict() + for leftLabel in leftLabels: + leftDict = {} + rightDict = {} + for rightLabel in rightLabels: + leftDict[rightLabel] = dataFrame[ + (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel) + ].leftWeight.sum() + + rightDict[rightLabel] = dataFrame[ + (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel) + ].rightWeight.sum() + factorleft = leftWidths_norm[leftLabel]['left']/sum(leftDict.values()) + leftDict_norm = {k: v*factorleft for k, v in leftDict.items()} + ns_l_norm[leftLabel] = leftDict_norm + ns_r[leftLabel] = rightDict + + # ns_r should be using a different way of normalization to fit the right side + # It is normalized using the value with the same key in each sub-dictionary + + ns_r_norm = normalize_dict(ns_r, rightWidths_norm) + + # Plot vertical bars for each label + for leftLabel in leftLabels: + ax.fill_between( + [leftpos + (-(bar_width) * xMax), leftpos], + 2 * [leftWidths_norm[leftLabel]["bottom"]], + 2 * [leftWidths_norm[leftLabel]["bottom"] + leftWidths_norm[leftLabel]["left"]], + color=colorDict[leftLabel], + alpha=0.99, + ) + for rightLabel in rightLabels: + ax.fill_between( + [xMax + leftpos, leftpos + ((1 + bar_width) * xMax)], + 2 * [rightWidths_norm[rightLabel]['bottom']], + 2 * [rightWidths_norm[rightLabel]['bottom'] + rightWidths_norm[rightLabel]['right']], + color=colorDict[rightLabel], + alpha=0.99 + ) + + # Plot error bars + error_bar(concatenated_df, x='groups', y='values', ax=ax, offset=0, gap_width_percent=2, + method="sankey_error_bar", + pos=[(leftpos + (-(bar_width) * xMax) + leftpos)/2, \ + (xMax + leftpos + leftpos + ((1 + bar_width) * xMax))/2]) + + # Plot strips + for leftLabel, rightLabel in itertools.product(leftLabels, rightLabels): + labelColor = leftLabel + if rightColor: + labelColor = rightLabel + if len(dataFrame[(dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)]) > 0: + # Create array of y values for each strip, half at left value, + # half at right, convolve + ys_d = np.array(50 * [leftWidths_norm[leftLabel]['bottom']] + \ + 50 * [rightWidths_norm[rightLabel]['bottom']]) + ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid') + ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid') + ys_u = np.array(50 * [leftWidths_norm[leftLabel]['bottom'] + ns_l_norm[leftLabel][rightLabel]] + \ + 50 * [rightWidths_norm[rightLabel]['bottom'] + ns_r_norm[leftLabel][rightLabel]]) + ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid') + ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid') + + # Update bottom edges at each label so next strip starts at the right place + leftWidths_norm[leftLabel]['bottom'] += ns_l_norm[leftLabel][rightLabel] + rightWidths_norm[rightLabel]['bottom'] += ns_r_norm[leftLabel][rightLabel] + ax.fill_between( + np.linspace(leftpos, leftpos + xMax, len(ys_d)), ys_d, ys_u, alpha=alpha, + color=colorDict[labelColor], edgecolor='none' + ) + +def sankeydiag(data:pd.DataFrame, + xvar:str, # x column to be plotted. + yvar:str, # y column to be plotted. + left_idx:str, #the value in column xvar that is on the left side of each sankey diagram + right_idx:str, #the value in column xvar that is on the right side of each sankey diagram, if len(left_idx) == 1, it will be broadcasted to the same length as right_idx, otherwise it should have the same length as right_idx + leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels. + rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels. + palette:str|dict=None, + ax=None, #matplotlib axes to be drawn on + one_sankey:bool=False,# determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes + width:float=0.4, # the width of each sankey diagram + rightColor:bool=False,#if True, each strip of the diagram will be colored according to the corresponding left labels + align:str='center', #the alignment of each sankey diagram, can be 'center' or 'left' + alpha:float=0.65, #the transparency of each strip + **kwargs): + ''' + Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes + using the value in column yvar according to the value in column xvar + left_idx in the column xvar is on the left side of each sankey diagram + right_idx in the column xvar is on the right side of each sankey diagram + + ''' + + import numpy as np + import pandas as pd + import seaborn as sns + import matplotlib.pyplot as plt + + if "width" in kwargs: + width = kwargs["width"] + + if "align" in kwargs: + align = kwargs["align"] + + if "alpha" in kwargs: + alpha = kwargs["alpha"] + + if "rightColor" in kwargs: + rightColor = kwargs["rightColor"] + + if "bar_width" in kwargs: + bar_width = kwargs["bar_width"] + + if ax is None: + ax = plt.gca() + + allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique() + + # Check if all the elements in left_idx and right_idx are in xvar column + unique_xvar = data[xvar].unique() + if not all(elem in unique_xvar for elem in left_idx): + raise ValueError(f"{left_idx} not found in {xvar} column") + if not all(elem in unique_xvar for elem in right_idx): + raise ValueError(f"{right_idx} not found in {xvar} column") + + xpos = 0 + + # For baseline comparison, broadcast left_idx to the same length as right_idx + # so that the left of sankey diagram will be the same + # For sequential comparison, left_idx and right_idx can have anything different + # but should have the same length + if len(left_idx) == 1: + broadcasted_left = np.broadcast_to(left_idx, len(right_idx)) + elif len(left_idx) != len(right_idx): + raise ValueError(f"left_idx and right_idx should have the same length") + else: + broadcasted_left = left_idx + + if isinstance(palette, dict): + if not all(key in allLabels for key in palette.keys()): + raise ValueError(f"keys in palette should be in {yvar} column") + else: + plot_palette = palette + elif isinstance(palette, str): + plot_palette = {} + colorPalette = sns.color_palette(palette, len(allLabels)) + for i, label in enumerate(allLabels): + plot_palette[label] = colorPalette[i] + else: + plot_palette = None + + for left, right in zip(broadcasted_left, right_idx): + if one_sankey == False: + single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], + xpos=xpos, ax=ax, colorDict=plot_palette, width=width, + leftLabels=leftLabels, rightLabels=rightLabels, + rightColor=rightColor, bar_width=bar_width, + align=align, alpha=alpha) + xpos += 1 + else: + xpos = 0 + bar_width/2 + width = 1 - bar_width + single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], + xpos=xpos, ax=ax, colorDict=plot_palette, width=width, + leftLabels=leftLabels, rightLabels=rightLabels, + rightColor=rightColor, bar_width=bar_width, + align='edge', alpha=alpha) + + if one_sankey == False: + sankey_ticks = [f"{left}\n v.s.\n{right}" for left, right in zip(broadcasted_left, right_idx)] + ax.get_xaxis().set_ticks(np.arange(len(right_idx))) + ax.get_xaxis().set_ticklabels(sankey_ticks) + else: + sankey_ticks = [broadcasted_left[0], right_idx[0]] + ax.set_xticks([0, 1]) + ax.set_xticklabels(sankey_ticks) + diff --git a/dabest/plotter.py b/dabest/plotter.py index 2bedd36d..f0167609 100644 --- a/dabest/plotter.py +++ b/dabest/plotter.py @@ -1,41 +1,39 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plotter.ipynb. +# %% auto 0 +__all__ = ['EffectSizeDataFramePlotter'] +# %% ../nbs/API/plotter.ipynb 4 def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): - """ Custom function that creates an estimation plot from an EffectSizeDataFrame. - - Keywords - -------- - EffectSizeDataFrame: A `dabest` EffectSizeDataFrame object. - - **plot_kwargs: + + Parameters + ---------- + EffectSizeDataFrame + A `dabest` EffectSizeDataFrame object. + plot_kwargs color_col=None - raw_marker_size=6, es_marker_size=9, - - swarm_label=None, contrast_label=None, - swarm_ylim=None, contrast_ylim=None, - + swarm_label=None, contrast_label=None, delta2_label=None, + swarm_ylim=None, contrast_ylim=None, delta2_ylim=None, custom_palette=None, swarm_desat=0.5, halfviolin_desat=1, - halfviolin_alpha=0.8, - + halfviolin_alpha=0.8, + face_color = None, + bar_label=None, bar_desat=0.8, bar_width = 0.5,bar_ylim = None, + ci=None, ci_type='bca', err_color=None, float_contrast=True, show_pairs=True, + show_delta2=True, group_summaries=None, group_summaries_offset=0.1, - fig_size=None, dpi=100, ax=None, - swarmplot_kwargs=None, violinplot_kwargs=None, slopegraph_kwargs=None, + sankey_kwargs=None, reflines_kwargs=None, group_summary_kwargs=None, legend_kwargs=None, @@ -45,9 +43,11 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): import seaborn as sns import matplotlib.pyplot as plt import pandas as pd + import warnings + warnings.filterwarnings('ignore', 'This figure includes Axes that are not compatible with tight_layout') from .misc_tools import merge_two_dicts - from .plot_tools import halfviolin, get_swarm_spans, gapped_lines + from .plot_tools import halfviolin, get_swarm_spans, error_bar, sankeydiag from ._stats_tools.effsize import _compute_standardizers, _compute_hedges_correction_factor import logging @@ -63,27 +63,40 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): plt.rcParams['axes.grid'] = False - ytick_color = plt.rcParams["ytick.color"] - axes_facecolor = plt.rcParams['axes.facecolor'] + face_color = plot_kwargs["face_color"] + if plot_kwargs["face_color"] is None: + face_color = "white" dabest_obj = EffectSizeDataFrame.dabest_obj plot_data = EffectSizeDataFrame._plot_data xvar = EffectSizeDataFrame.xvar yvar = EffectSizeDataFrame.yvar is_paired = EffectSizeDataFrame.is_paired - + delta2 = EffectSizeDataFrame.delta2 + mini_meta = EffectSizeDataFrame.mini_meta + effect_size = EffectSizeDataFrame.effect_size + proportional = EffectSizeDataFrame.proportional all_plot_groups = dabest_obj._all_plot_groups idx = dabest_obj.idx + if effect_size != "mean_diff" or not delta2: + show_delta2 = False + else: + show_delta2 = plot_kwargs["show_delta2"] + if effect_size != "mean_diff" or not mini_meta: + show_mini_meta = False + else: + show_mini_meta = plot_kwargs["show_mini_meta"] - - + if show_delta2 and show_mini_meta: + err0 = "`show_delta2` and `show_mini_meta` cannot be True at the same time." + raise ValueError(err0) # Disable Gardner-Altman plotting if any of the idxs comprise of more than - # two groups. + # two groups or if it is a delta-delta plot. float_contrast = plot_kwargs["float_contrast"] effect_size_type = EffectSizeDataFrame.effect_size if len(idx) > 1 or len(idx[0]) > 2: @@ -92,19 +105,14 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if effect_size_type in ['cliffs_delta']: float_contrast = False + if show_delta2 or show_mini_meta: + float_contrast = False - - # Disable slopegraph plotting if any of the idxs comprise of more than - # two groups. - if np.all([len(i)==2 for i in idx]) is False: - is_paired = False - # if paired is False, set show_pairs as False. - if is_paired is False: + if not is_paired: show_pairs = False else: show_pairs = plot_kwargs["show_pairs"] - # Set default kwargs first, then merge with user-dictated ones. default_swarmplot_kwargs = {'size': plot_kwargs["raw_marker_size"]} if plot_kwargs["swarmplot_kwargs"] is None: @@ -113,7 +121,25 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): swarmplot_kwargs = merge_two_dicts(default_swarmplot_kwargs, plot_kwargs["swarmplot_kwargs"]) + # Barplot kwargs + default_barplot_kwargs = {"estimator": np.mean, "ci": plot_kwargs["ci"]} + if plot_kwargs["barplot_kwargs"] is None: + barplot_kwargs = default_barplot_kwargs + else: + barplot_kwargs = merge_two_dicts(default_barplot_kwargs, + plot_kwargs["barplot_kwargs"]) + + # Sankey Diagram kwargs + default_sankey_kwargs = {"width": 0.4, "align": "center", + "alpha": 0.4, "rightColor": False, + "bar_width":0.2} + if plot_kwargs["sankey_kwargs"] is None: + sankey_kwargs = default_sankey_kwargs + else: + sankey_kwargs = merge_two_dicts(default_sankey_kwargs, + plot_kwargs["sankey_kwargs"]) + # Violinplot kwargs. default_violinplot_kwargs = {'widths':0.5, 'vert':True, @@ -124,7 +150,6 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): violinplot_kwargs = merge_two_dicts(default_violinplot_kwargs, plot_kwargs["violinplot_kwargs"]) - # slopegraph kwargs. default_slopegraph_kwargs = {'lw':1, 'alpha':0.5} if plot_kwargs["slopegraph_kwargs"] is None: @@ -133,9 +158,6 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): slopegraph_kwargs = merge_two_dicts(default_slopegraph_kwargs, plot_kwargs["slopegraph_kwargs"]) - - - # Zero reference-line kwargs. default_reflines_kwargs = {'linestyle':'solid', 'linewidth':0.75, 'zorder': 2, @@ -146,8 +168,6 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): reflines_kwargs = merge_two_dicts(default_reflines_kwargs, plot_kwargs["reflines_kwargs"]) - - # Legend kwargs. default_legend_kwargs = {'loc': 'upper left', 'frameon': False} if plot_kwargs["legend_kwargs"] is None: @@ -156,7 +176,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): legend_kwargs = merge_two_dicts(default_legend_kwargs, plot_kwargs["legend_kwargs"]) - + # Group summaries kwargs. gs_default = {'mean_sd', 'median_quartiles', None} if plot_kwargs["group_summaries"] not in gs_default: raise ValueError('group_summaries must be one of' @@ -170,8 +190,6 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): group_summary_kwargs = merge_two_dicts(default_group_summary_kwargs, plot_kwargs["group_summary_kwargs"]) - - # Create color palette that will be shared across subplots. color_col = plot_kwargs["color_col"] if color_col is None: @@ -190,6 +208,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): n_groups = len(color_groups) custom_pal = plot_kwargs["custom_palette"] swarm_desat = plot_kwargs["swarm_desat"] + bar_desat = plot_kwargs["bar_desat"] contrast_desat = plot_kwargs["halfviolin_desat"] if custom_pal is None: @@ -226,18 +245,40 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): err2 = ' is not a matplotlib palette. Please check.' raise ValueError(err1 + err2) - swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors] - plot_palette_raw = dict(zip(names, swarm_colors)) + if custom_pal is None and color_col is None: + swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors] + plot_palette_raw = dict(zip(names.categories, swarm_colors)) + + bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors] + plot_palette_bar = dict(zip(names.categories, bar_color)) + + contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors] + plot_palette_contrast = dict(zip(names.categories, contrast_colors)) + + # For Sankey Diagram plot, no need to worry about the color, each bar will have the same two colors + # default color palette will be set to "hls" + plot_palette_sankey = None - contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors] - plot_palette_contrast = dict(zip(names, contrast_colors)) + else: + swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors] + plot_palette_raw = dict(zip(names, swarm_colors)) + + bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors] + plot_palette_bar = dict(zip(names, bar_color)) + contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors] + plot_palette_contrast = dict(zip(names, contrast_colors)) + + plot_palette_sankey = custom_pal # Infer the figsize. fig_size = plot_kwargs["fig_size"] if fig_size is None: all_groups_count = np.sum([len(i) for i in dabest_obj.idx]) - if is_paired is True and show_pairs is True: + # Increase the width for delta-delta graph + if show_delta2 or show_mini_meta: + all_groups_count += 2 + if is_paired and show_pairs is True and proportional is False: frac = 0.75 else: frac = 1 @@ -251,12 +292,10 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): width_inches = (each_group_width_inches * all_groups_count) fig_size = (width_inches, height_inches) - - # Initialise the figure. # sns.set(context="talk", style='ticks') - init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"], - tight_layout=True) + init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"] + ,tight_layout=True) width_ratios_ga = [2.5, 1] h_space_cummings = 0.3 @@ -269,6 +308,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): ax_position = rawdata_axes.get_position() # [[x0, y0], [x1, y1]] fig = rawdata_axes.get_figure() + fig.patch.set_facecolor(face_color) if float_contrast is True: axins = rawdata_axes.inset_axes( @@ -311,11 +351,13 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): gridspec_kw={"width_ratios": width_ratios_ga, "wspace": 0}, **init_fig_kwargs) + fig.patch.set_facecolor(face_color) else: fig, axx = plt.subplots(nrows=2, gridspec_kw={"hspace": 0.3}, **init_fig_kwargs) + fig.patch.set_facecolor(face_color) # If the contrast axes are NOT floating, create lists to store # raw ylims and raw tick intervals, so that I can normalize # their ylims later. @@ -325,7 +367,6 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): rawdata_axes = axx[0] contrast_axes = axx[1] - rawdata_axes.set_frame_on(False) contrast_axes.set_frame_on(False) @@ -340,65 +381,132 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if swarm_ylim is not None: rawdata_axes.set_ylim(swarm_ylim) + one_sankey = None + if is_paired is not None: + one_sankey = False # Flag to indicate if only one sankey is plotted. + if show_pairs is True: + # Determine temp_idx based on is_paired and proportional conditions + if is_paired == "baseline": + idx_pairs = [(control, test) for i in idx for control, test in zip([i[0]] * (len(i) - 1), i[1:])] + temp_idx = idx if not proportional else idx_pairs + else: + idx_pairs = [(control, test) for i in idx for control, test in zip(i[:-1], i[1:])] + temp_idx = idx if not proportional else idx_pairs + + # Determine temp_all_plot_groups based on proportional condition + plot_groups = [item for i in temp_idx for item in i] + temp_all_plot_groups = all_plot_groups if not proportional else plot_groups + + if proportional==False: # Plot the raw data as a slopegraph. # Pivot the long (melted) data. - if color_col is None: - pivot_values = yvar - else: - pivot_values = [yvar, color_col] - pivoted_plot_data = pd.pivot(data=plot_data, index=dabest_obj.id_col, - columns=xvar, values=pivot_values) - - for ii, current_tuple in enumerate(idx): - if len(idx) > 1: - # Select only the data for the current tuple. - if color_col is None: - current_pair = pivoted_plot_data.reindex(columns=current_tuple) - else: - current_pair = pivoted_plot_data[yvar].reindex(columns=current_tuple) + if color_col is None: + pivot_values = yvar else: - if color_col is None: - current_pair = pivoted_plot_data + pivot_values = [yvar, color_col] + pivoted_plot_data = pd.pivot(data=plot_data, index=dabest_obj.id_col, + columns=xvar, values=pivot_values) + x_start = 0 + for ii, current_tuple in enumerate(temp_idx): + if len(temp_idx) > 1: + # Select only the data for the current tuple. + if color_col is None: + current_pair = pivoted_plot_data.reindex(columns=current_tuple) + else: + current_pair = pivoted_plot_data[yvar].reindex(columns=current_tuple) else: - current_pair = pivoted_plot_data[yvar] - - # Iterate through the data for the current tuple. - for ID, observation in current_pair.iterrows(): - x_start = (ii * 2) - x_points = [x_start, x_start+1] - y_points = observation.tolist() - - if color_col is None: - slopegraph_kwargs['color'] = ytick_color - else: - color_key = pivoted_plot_data[color_col, - current_tuple[0]].loc[ID] - slopegraph_kwargs['color'] = plot_palette_raw[color_key] - slopegraph_kwargs['label'] = color_key - - rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs) - - # Set the tick labels, because the slopegraph plotting doesn't. - rawdata_axes.set_xticks(np.arange(0, len(all_plot_groups))) - rawdata_axes.set_xticklabels(all_plot_groups) - + if color_col is None: + current_pair = pivoted_plot_data + else: + current_pair = pivoted_plot_data[yvar] + grp_count = len(current_tuple) + # Iterate through the data for the current tuple. + for ID, observation in current_pair.iterrows(): + x_points = [t for t in range(x_start, x_start + grp_count)] + y_points = observation.tolist() + + if color_col is None: + slopegraph_kwargs['color'] = ytick_color + else: + color_key = pivoted_plot_data[color_col, + current_tuple[0]].loc[ID] + if isinstance(color_key, str) == True: + slopegraph_kwargs['color'] = plot_palette_raw[color_key] + slopegraph_kwargs['label'] = color_key + + rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs) + x_start = x_start + grp_count + # Set the tick labels, because the slopegraph plotting doesn't. + rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups))) + rawdata_axes.set_xticklabels(temp_all_plot_groups) + + else: + # Plot the raw data as a set of Sankey Diagrams aligned like barplot. + group_summaries = plot_kwargs["group_summaries"] + if group_summaries is None: + group_summaries = "mean_sd" + err_color = plot_kwargs["err_color"] + if err_color == None: + err_color = "black" + if show_pairs is True: + sankey_control_group = [] + sankey_test_group = [] + for i in temp_idx: + sankey_control_group.append(i[0]) + sankey_test_group.append(i[1]) + + if len(temp_all_plot_groups) == 2: + one_sankey = True + + # Replace the paired proportional plot with sankey diagram + sankey = sankeydiag(plot_data, xvar=xvar, yvar=yvar, + left_idx=sankey_control_group, + right_idx=sankey_test_group, + palette=plot_palette_sankey, + ax=rawdata_axes, + one_sankey=one_sankey, + **sankey_kwargs) + else: - # Plot the raw data as a swarmplot. - rawdata_plot = sns.swarmplot(data=plot_data, x=xvar, y=yvar, - ax=rawdata_axes, - order=all_plot_groups, hue=color_col, - palette=plot_palette_raw, zorder=1, - **swarmplot_kwargs) + if proportional==False: + # Plot the raw data as a swarmplot. + rawdata_plot = sns.swarmplot(data=plot_data, x=xvar, y=yvar, + ax=rawdata_axes, + order=all_plot_groups, hue=color_col, + palette=plot_palette_raw, zorder=1, + **swarmplot_kwargs) + else: + # Plot the raw data as a barplot. + bar1_df = pd.DataFrame({xvar: all_plot_groups, 'proportion': np.ones(len(all_plot_groups))}) + bar1 = sns.barplot(data=bar1_df, x=xvar, y="proportion", + ax=rawdata_axes, + order=all_plot_groups, + linewidth=2, facecolor=(1, 1, 1, 0), edgecolor=bar_color, + zorder=1) + bar2 = sns.barplot(data=plot_data, x=xvar, y=yvar, + ax=rawdata_axes, + order=all_plot_groups, + palette=plot_palette_bar, + zorder=1, + **barplot_kwargs) + # adjust the width of bars + bar_width = plot_kwargs["bar_width"] + for bar in bar1.patches: + x = bar.get_x() + width = bar.get_width() + centre = x + width / 2. + bar.set_x(centre - bar_width / 2.) + bar.set_width(bar_width) # Plot the gapped line summaries, if this is not a Cumming plot. # Also, we will not plot gapped lines for paired plots. For now. group_summaries = plot_kwargs["group_summaries"] - if float_contrast is False and group_summaries is None: + if group_summaries is None: group_summaries = "mean_sd" - if group_summaries is not None: + if group_summaries is not None and proportional==False: # Create list to gather xspans. xspans = [] line_colors = [] @@ -417,28 +525,45 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if len(line_colors) != len(all_plot_groups): line_colors = ytick_color - gapped_lines(plot_data, x=xvar, y=yvar, + error_bar(plot_data, x=xvar, y=yvar, # Hardcoded offset... offset=xspans + np.array(plot_kwargs["group_summaries_offset"]), line_color=line_colors, gap_width_percent=1.5, type=group_summaries, ax=rawdata_axes, + method="gapped_lines", **group_summary_kwargs) + if group_summaries is not None and proportional == True: + + err_color = plot_kwargs["err_color"] + if err_color == None: + err_color = "black" + error_bar(plot_data, x=xvar, y=yvar, + offset=0, + line_color=err_color, + gap_width_percent=1.5, + type=group_summaries, ax=rawdata_axes, + method="proportional_error_bar", + **group_summary_kwargs) # Add the counts to the rawdata axes xticks. counts = plot_data.groupby(xvar).count()[yvar] ticks_with_counts = [] for xticklab in rawdata_axes.xaxis.get_ticklabels(): t = xticklab.get_text() - N = str(counts.loc[t]) + if t.rfind("\n") != -1: + te = t[t.rfind("\n") + len("\n"):] + N = str(counts.loc[te]) + te = t + else: + te = t + N = str(counts.loc[te]) - ticks_with_counts.append("{}\nN = {}".format(t, N)) + ticks_with_counts.append("{}\nN = {}".format(te, N)) rawdata_axes.set_xticklabels(ticks_with_counts) - - # Save the handles and labels for the legend. handles, labels = rawdata_axes.get_legend_handles_labels() legend_labels = [l for l in labels] @@ -446,30 +571,68 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if bootstraps_color_by_group is False: rawdata_axes.legend().set_visible(False) + # Enforce the xtick of rawdata_axes to be 0 and 1 after drawing only one sankey + if one_sankey: + rawdata_axes.set_xticks([0, 1]) + # Plot effect sizes and bootstraps. # Take note of where the `control` groups are. - ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist() - ticks_to_skip.insert(0, 0) - - # Then obtain the ticks where we have to plot the effect sizes. - ticks_to_plot = [t for t in range(0, len(all_plot_groups)) - if t not in ticks_to_skip] + if is_paired == "baseline" and show_pairs == True: + if proportional == True and one_sankey == False: + ticks_to_skip = [] + ticks_to_plot = np.arange(0, len(temp_all_plot_groups)/2).tolist() + ticks_to_start_sankey = np.cumsum([len(i)-1 for i in idx]).tolist() + ticks_to_start_sankey.pop() + ticks_to_start_sankey.insert(0, 0) + else: + # ticks_to_skip = np.arange(0, len(temp_all_plot_groups), 2).tolist() + # ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist() + ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist() + ticks_to_skip.insert(0, 0) + # Then obtain the ticks where we have to plot the effect sizes. + ticks_to_plot = [t for t in range(0, len(all_plot_groups)) + if t not in ticks_to_skip] + ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist() + ticks_to_skip_contrast.insert(0, 0) + else: + if proportional == True and one_sankey == False: + ticks_to_skip = [len(sankey_control_group)] + # Then obtain the ticks where we have to plot the effect sizes. + ticks_to_plot = [t for t in range(0, len(temp_idx)) + if t not in ticks_to_skip] + ticks_to_skip = [] + ticks_to_start_sankey = np.cumsum([len(i)-1 for i in idx]).tolist() + ticks_to_start_sankey.pop() + ticks_to_start_sankey.insert(0, 0) + else: + ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist() + ticks_to_skip.insert(0, 0) + # Then obtain the ticks where we have to plot the effect sizes. + ticks_to_plot = [t for t in range(0, len(all_plot_groups)) + if t not in ticks_to_skip] # Plot the bootstraps, then the effect sizes and CIs. es_marker_size = plot_kwargs["es_marker_size"] halfviolin_alpha = plot_kwargs["halfviolin_alpha"] + ci_type = plot_kwargs["ci_type"] results = EffectSizeDataFrame.results contrast_xtick_labels = [] + for j, tick in enumerate(ticks_to_plot): current_group = results.test[j] current_control = results.control[j] current_bootstrap = results.bootstraps[j] current_effsize = results.difference[j] - current_ci_low = results.bca_low[j] - current_ci_high = results.bca_high[j] + if ci_type == "bca": + current_ci_low = results.bca_low[j] + current_ci_high = results.bca_high[j] + else: + current_ci_low = results.pct_low[j] + current_ci_high = results.pct_high[j] + # Create the violinplot. # New in v0.2.6: drop negative infinities before plotting. @@ -501,28 +664,90 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): contrast_xtick_labels.append("{}\nminus\n{}".format(current_group, current_control)) + # Plot mini-meta violin + if show_mini_meta or show_delta2: + if show_mini_meta: + mini_meta_delta = EffectSizeDataFrame.mini_meta_delta + data = mini_meta_delta.bootstraps_weighted_delta + difference = mini_meta_delta.difference + if ci_type == "bca": + ci_low = mini_meta_delta.bca_low + ci_high = mini_meta_delta.bca_high + else: + ci_low = mini_meta_delta.pct_low + ci_high = mini_meta_delta.pct_high + else: + delta_delta = EffectSizeDataFrame.delta_delta + data = delta_delta.bootstraps_delta_delta + difference = delta_delta.difference + if ci_type == "bca": + ci_low = delta_delta.bca_low + ci_high = delta_delta.bca_high + else: + ci_low = delta_delta.pct_low + ci_high = delta_delta.pct_high + #Create the violinplot. + #New in v0.2.6: drop negative infinities before plotting. + position = max(rawdata_axes.get_xticks())+2 + v = contrast_axes.violinplot(data[~np.isinf(data)], + positions=[position], + **violinplot_kwargs) + + fc = "grey" + + halfviolin(v, fill_color=fc, alpha=halfviolin_alpha) + + # Plot the effect size. + contrast_axes.plot([position], difference, marker='o', + color=ytick_color, + markersize=es_marker_size) + # Plot the confidence interval. + contrast_axes.plot([position, position], + [ci_low, ci_high], + linestyle="-", + color=ytick_color, + linewidth=group_summary_kwargs['lw']) + if show_mini_meta: + contrast_xtick_labels.extend(["","Weighted delta"]) + else: + contrast_xtick_labels.extend(["","delta-delta"]) + + # Make sure the contrast_axes x-lims match the rawdata_axes xlims, + # and add an extra violinplot tick for delta-delta plot. + if show_delta2 is False and show_mini_meta is False: + contrast_axes.set_xticks(rawdata_axes.get_xticks()) + else: + temp = rawdata_axes.get_xticks() + temp = np.append(temp, [max(temp)+1, max(temp)+2]) + contrast_axes.set_xticks(temp) - # Make sure the contrast_axes x-lims match the rawdata_axes xlims. - contrast_axes.set_xticks(rawdata_axes.get_xticks()) if show_pairs is True: max_x = contrast_axes.get_xlim()[1] rawdata_axes.set_xlim(-0.375, max_x) if float_contrast is True: contrast_axes.set_xlim(0.5, 1.5) + elif show_delta2 or show_mini_meta: + # Increase the xlim of raw data by 2 + temp = rawdata_axes.get_xlim() + if show_pairs: + rawdata_axes.set_xlim(temp[0], temp[1]+0.25) + else: + rawdata_axes.set_xlim(temp[0], temp[1]+2) + contrast_axes.set_xlim(rawdata_axes.get_xlim()) else: contrast_axes.set_xlim(rawdata_axes.get_xlim()) # Properly label the contrast ticks. for t in ticks_to_skip: contrast_xtick_labels.insert(t, "") + contrast_axes.set_xticklabels(contrast_xtick_labels) - if bootstraps_color_by_group is False: legend_labels_unique = np.unique(legend_labels) unique_idx = np.unique(legend_labels, return_index=True)[1] - legend_handles_unique = (pd.Series(legend_handles).loc[unique_idx]).tolist() + legend_handles_unique = (pd.Series(legend_handles, dtype="object").loc[unique_idx]).tolist() if len(legend_handles_unique) > 0: if float_contrast is True: @@ -545,20 +770,19 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): for line in leg.get_lines(): line.set_linewidth(3.0) - - og_ylim_raw = rawdata_axes.get_ylim() + og_xlim_raw = rawdata_axes.get_xlim() if float_contrast is True: # For Gardner-Altman plots only. # Normalize ylims and despine the floating contrast axes. # Check that the effect size is within the swarm ylims. - if effect_size_type in ["mean_diff", "cohens_d", "hedges_g"]: + if effect_size_type in ["mean_diff", "cohens_d", "hedges_g","cohens_h"]: control_group_summary = plot_data.groupby(xvar)\ - .mean().loc[current_control, yvar] + .mean(numeric_only=True).loc[current_control, yvar] test_group_summary = plot_data.groupby(xvar)\ - .mean().loc[current_group, yvar] + .mean(numeric_only=True).loc[current_group, yvar] elif effect_size_type == "median_diff": control_group_summary = plot_data.groupby(xvar)\ .median().loc[current_control, yvar] @@ -591,7 +815,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): contrast_axes.set_ylim(og_ylim_contrast) contrast_axes.set_xlim(contrast_xlim_max-1, contrast_xlim_max) - elif effect_size_type in ["cohens_d", "hedges_g"]: + elif effect_size_type in ["cohens_d", "hedges_g","cohens_h"]: if is_paired: which_std = 1 else: @@ -613,7 +837,10 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): hg_correction_factor = _compute_hedges_correction_factor(len_control, len_test) ylim_scale_factor = pooled_sd / hg_correction_factor - + + elif effect_size_type == "cohens_h": + ylim_scale_factor = (np.mean(temp_test)-np.mean(temp_control)) / difference + else: ylim_scale_factor = pooled_sd @@ -624,33 +851,47 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): contrast_axes.set_xlim(contrast_xlim_max-1, contrast_xlim_max) + if one_sankey is None: + # Draw summary lines for control and test groups.. + for jj, axx in enumerate([rawdata_axes, contrast_axes]): + # Draw effect size line. + if jj == 0: + ref = control_group_summary + diff = test_group_summary + effsize_line_start = 1 - # Draw summary lines for control and test groups.. - for jj, axx in enumerate([rawdata_axes, contrast_axes]): - - # Draw effect size line. - if jj == 0: - ref = control_group_summary - diff = test_group_summary - effsize_line_start = 1 + elif jj == 1: + ref = 0 + diff = ref + difference + effsize_line_start = contrast_xlim_max-1.1 - elif jj == 1: - ref = 0 - diff = ref + difference - effsize_line_start = contrast_xlim_max-1.1 - - xlimlow, xlimhigh = axx.get_xlim() + xlimlow, xlimhigh = axx.get_xlim() + # Draw reference line. + axx.hlines(ref, # y-coordinates + 0, xlimhigh, # x-coordinates, start and end. + **reflines_kwargs) + + # Draw effect size line. + axx.hlines(diff, + effsize_line_start, xlimhigh, + **reflines_kwargs) + else: + ref = 0 + diff = ref + difference + effsize_line_start = contrast_xlim_max - 0.9 + xlimlow, xlimhigh = contrast_axes.get_xlim() # Draw reference line. - axx.hlines(ref, # y-coordinates - 0, xlimhigh, # x-coordinates, start and end. - **reflines_kwargs) + contrast_axes.hlines(ref, # y-coordinates + effsize_line_start, xlimhigh, # x-coordinates, start and end. + **reflines_kwargs) # Draw effect size line. - axx.hlines(diff, effsize_line_start, xlimhigh, - **reflines_kwargs) - + contrast_axes.hlines(diff, + effsize_line_start, xlimhigh, + **reflines_kwargs) + rawdata_axes.set_xlim(og_xlim_raw) # to align the axis # Despine appropriately. sns.despine(ax=rawdata_axes, bottom=True) sns.despine(ax=contrast_axes, left=True, right=False) @@ -671,8 +912,19 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): # For Cumming Plots only. # Set custom contrast_ylim, if it was specified. - if plot_kwargs['contrast_ylim'] is not None: - custom_contrast_ylim = plot_kwargs['contrast_ylim'] + if plot_kwargs['contrast_ylim'] is not None or (plot_kwargs['delta2_ylim'] is not None and show_delta2): + + if plot_kwargs['contrast_ylim'] is not None: + custom_contrast_ylim = plot_kwargs['contrast_ylim'] + if plot_kwargs['delta2_ylim'] is not None and show_delta2: + custom_delta2_ylim = plot_kwargs['delta2_ylim'] + if custom_contrast_ylim!=custom_delta2_ylim: + err1 = "Please check if `contrast_ylim` and `delta2_ylim` are assigned" + err2 = "with same values." + raise ValueError(err1 + err2) + else: + custom_delta2_ylim = plot_kwargs['delta2_ylim'] + custom_contrast_ylim = custom_delta2_ylim if len(custom_contrast_ylim) != 2: err1 = "Please check `contrast_ylim` consists of " @@ -699,26 +951,94 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if contrast_ylim_low < 0 < contrast_ylim_high: contrast_axes.axhline(y=0, **reflines_kwargs) - # Compute the end of each x-axes line. - rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip) + if is_paired == "baseline" and show_pairs == True: + if proportional == True and one_sankey == False: + rightend_ticks_raw = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey) + else: + rightend_ticks_raw = np.array([len(i)-1 for i in temp_idx]) + np.array(ticks_to_skip) + for ax in [rawdata_axes]: + sns.despine(ax=ax, bottom=True) - for ax in [rawdata_axes, contrast_axes]: - sns.despine(ax=ax, bottom=True) + ylim = ax.get_ylim() + xlim = ax.get_xlim() + redraw_axes_kwargs['y'] = ylim[0] - ylim = ax.get_ylim() - xlim = ax.get_xlim() - redraw_axes_kwargs['y'] = ylim[0] + if proportional == True and one_sankey == False: + for k, start_tick in enumerate(ticks_to_start_sankey): + end_tick = rightend_ticks_raw[k] + ax.hlines(xmin=start_tick, xmax=end_tick, + **redraw_axes_kwargs) + else: + for k, start_tick in enumerate(ticks_to_skip): + end_tick = rightend_ticks_raw[k] + ax.hlines(xmin=start_tick, xmax=end_tick, + **redraw_axes_kwargs) + ax.set_ylim(ylim) + del redraw_axes_kwargs['y'] + + if proportional == False: + temp_length = [(len(i)-1) for i in idx] + else: + temp_length = [(len(i)-1)*2-1 for i in idx] + if proportional == True and one_sankey == False: + rightend_ticks_contrast = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey) + else: + rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast) + for ax in [contrast_axes]: + sns.despine(ax=ax, bottom=True) - for k, start_tick in enumerate(ticks_to_skip): - end_tick = rightend_ticks[k] - ax.hlines(xmin=start_tick, xmax=end_tick, - **redraw_axes_kwargs) + ylim = ax.get_ylim() + xlim = ax.get_xlim() + redraw_axes_kwargs['y'] = ylim[0] - ax.set_ylim(ylim) - del redraw_axes_kwargs['y'] - - + if proportional == True and one_sankey == False: + for k, start_tick in enumerate(ticks_to_start_sankey): + end_tick = rightend_ticks_contrast[k] + ax.hlines(xmin=start_tick, xmax=end_tick, + **redraw_axes_kwargs) + else: + for k, start_tick in enumerate(ticks_to_skip_contrast): + end_tick = rightend_ticks_contrast[k] + ax.hlines(xmin=start_tick, xmax=end_tick, + **redraw_axes_kwargs) + + ax.set_ylim(ylim) + del redraw_axes_kwargs['y'] + else: + # Compute the end of each x-axes line. + if proportional == True and one_sankey == False: + rightend_ticks = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey) + else: + rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip) + + for ax in [rawdata_axes, contrast_axes]: + sns.despine(ax=ax, bottom=True) + + ylim = ax.get_ylim() + xlim = ax.get_xlim() + redraw_axes_kwargs['y'] = ylim[0] + + if proportional == True and one_sankey == False: + for k, start_tick in enumerate(ticks_to_start_sankey): + end_tick = rightend_ticks[k] + ax.hlines(xmin=start_tick, xmax=end_tick, + **redraw_axes_kwargs) + else: + for k, start_tick in enumerate(ticks_to_skip): + end_tick = rightend_ticks[k] + ax.hlines(xmin=start_tick, xmax=end_tick, + **redraw_axes_kwargs) + + ax.set_ylim(ylim) + del redraw_axes_kwargs['y'] + if show_delta2 is True or show_mini_meta is True: + ylim = contrast_axes.get_ylim() + redraw_axes_kwargs['y'] = ylim[0] + x_ticks = contrast_axes.get_xticks() + contrast_axes.hlines(xmin=x_ticks[-2], xmax=x_ticks[-1], + **redraw_axes_kwargs) + del redraw_axes_kwargs['y'] # Set raw axes y-label. swarm_label = plot_kwargs['swarm_label'] @@ -726,17 +1046,29 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): swarm_label = "value" elif swarm_label is None and yvar is not None: swarm_label = yvar - + + bar_label = plot_kwargs['bar_label'] + if bar_label is None and effect_size_type != "cohens_h": + bar_label = "proportion of success" + elif bar_label is None and effect_size_type == "cohens_h": + bar_label = "value" + # Place contrast axes y-label. - contrast_label_dict = {'mean_diff' : "mean difference", - 'median_diff' : "median difference", - 'cohens_d' : "Cohen's d", - 'hedges_g' : "Hedges' g", - 'cliffs_delta' : "Cliff's delta"} - default_contrast_label = contrast_label_dict[EffectSizeDataFrame.effect_size] + contrast_label_dict = {'mean_diff': "mean difference", + 'median_diff': "median difference", + 'cohens_d': "Cohen's d", + 'hedges_g': "Hedges' g", + 'cliffs_delta': "Cliff's delta", + 'cohens_h': "Cohen's h"} + + if proportional == True and effect_size_type != "cohens_h": + default_contrast_label = "proportion difference" + else: + default_contrast_label = contrast_label_dict[EffectSizeDataFrame.effect_size] + if plot_kwargs['contrast_label'] is None: - if is_paired is True: + if is_paired: contrast_label = "paired\n{}".format(default_contrast_label) else: contrast_label = default_contrast_label @@ -748,15 +1080,17 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if float_contrast is True: contrast_axes.yaxis.set_label_position("right") - - # Set the rawdata axes labels appropriately - rawdata_axes.set_ylabel(swarm_label) + # Set the rawdata axes labels appropriately + if proportional == False: + rawdata_axes.set_ylabel(swarm_label) + else: + rawdata_axes.set_ylabel(bar_label) rawdata_axes.set_xlabel("") - - # Because we turned the axes frame off, we also need to draw back # the y-spine for both axes. + if float_contrast==False: + rawdata_axes.set_xlim(contrast_axes.get_xlim()) og_xlim_raw = rawdata_axes.get_xlim() rawdata_axes.vlines(og_xlim_raw[0], og_ylim_raw[0], og_ylim_raw[1], @@ -775,6 +1109,20 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): **redraw_axes_kwargs) + if show_delta2 is True: + if plot_kwargs['delta2_label'] is None: + delta2_label = "delta - delta" + else: + delta2_label = plot_kwargs['delta2_label'] + delta2_axes = contrast_axes.twinx() + delta2_axes.set_frame_on(False) + delta2_axes.set_ylabel(delta2_label) + og_xlim_delta = contrast_axes.get_xlim() + og_ylim_delta = contrast_axes.get_ylim() + delta2_axes.set_ylim(og_ylim_delta) + delta2_axes.vlines(og_xlim_delta[1], + og_ylim_delta[0], og_ylim_delta[1], + **redraw_axes_kwargs) # Make sure no stray ticks appear! rawdata_axes.xaxis.set_ticks_position('bottom') @@ -783,13 +1131,9 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if float_contrast is False: contrast_axes.yaxis.set_ticks_position('left') - - # Reset rcParams. for parameter in _changed_rcParams: plt.rcParams[parameter] = original_rcParams[parameter] - - # Return the figure. return fig diff --git a/dabest/tests/README.md b/dabest/tests/README.md deleted file mode 100644 index e0fab09a..00000000 --- a/dabest/tests/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# Testing - -We use [pytest](https://docs.pytest.org/en/latest) to execute the tests. For testing of plot generation, we use the [mpl plugin](https://github.com/matplotlib/pytest-mpl) for pytest. A range of different plots are created, and compared against the baseline images in the `baseline_images` subfolder. - -To run the tests, go to the root of this repo directory and run -```shell -pytest dabest -``` - - diff --git a/dabest/tests/__init__.py b/dabest/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/dabest/tests/baseline_images/test_01_gardner_altman_unpaired_meandiff.png b/dabest/tests/baseline_images/test_01_gardner_altman_unpaired_meandiff.png deleted file mode 100644 index bb0bdbb6..00000000 Binary files a/dabest/tests/baseline_images/test_01_gardner_altman_unpaired_meandiff.png and /dev/null differ diff --git a/dabest/tests/baseline_images/test_02_gardner_altman_unpaired_mediandiff.png b/dabest/tests/baseline_images/test_02_gardner_altman_unpaired_mediandiff.png deleted file mode 100644 index dabebe4a..00000000 Binary files a/dabest/tests/baseline_images/test_02_gardner_altman_unpaired_mediandiff.png and /dev/null differ diff --git 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as sp -import pandas as pd -from .._stats_tools import effsize -from .._classes import TwoGroupsEffectSize, PermutationTest, Dabest - - - -# Data for tests. -# See Cumming, G. Understanding the New Statistics: -# Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, 2012, -# from Cumming 2012 Table 11.1 Pg 287. -wb = {"control": [34, 54, 33, 44, 45, 53, 37, 26, 38, 58], - "expt": [66, 38, 35, 55, 48, 39, 65, 32, 57, 41]} -wellbeing = pd.DataFrame(wb) - - - -# from Cumming 2012 Table 11.2 Page 291 -paired_wb = {"pre": [43, 28, 54, 36, 31, 48, 50, 69, 29, 40], - "post": [51, 33, 58, 42, 39, 45, 54, 68, 35, 44], - "ID": np.arange(10)} -paired_wellbeing = pd.DataFrame(paired_wb) - - - -# Data from Hogarty and Kromrey (1999) -# Kromrey, Jeffrey D., and Kristine Y. Hogarty. 1998. -# “Analysis Options for Testing Group Differences on Ordered Categorical -# Variables: An Empirical Investigation of Type I Error Control -# Statistical Power.” -# Multiple Linear Regression Viewpoints 25 (1): 70 - 82. -likert_control = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5] -likert_treatment = [1, 2, 3, 4, 4, 5] - - - -# Data from Cliff (1993) -# Cliff, Norman. 1993. “Dominance Statistics: Ordinal Analyses to Answer -# Ordinal Questions.” -# Psychological Bulletin 114 (3): 494–509. -a_scores = [6, 7, 9, 10] -b_scores = [1, 3, 4, 7, 8] - - - -# kwargs for Dabest class init. -dabest_default_kwargs = dict(x=None, y=None, ci=95, - resamples=5000, random_seed=12345) - - - -def test_mean_diff_unpaired(): - import numpy as np - mean_diff = effsize.func_difference(wellbeing.control, wellbeing.expt, - np.mean, is_paired=False) - assert mean_diff == pytest.approx(5.4) - - - -def test_median_diff_unpaired(): - from numpy import median as npmedian - median_diff = effsize.func_difference(wellbeing.control, wellbeing.expt, - npmedian, is_paired=False) - assert median_diff == pytest.approx(3.5) - - - -def test_mean_diff_paired(): - from numpy import mean as npmean - mean_diff = effsize.func_difference(paired_wellbeing.pre, - paired_wellbeing.post, - npmean, is_paired=True) - assert mean_diff == pytest.approx(4.10) - - - -def test_median_diff_paired(): - from numpy import median as npmedian - median_diff = effsize.func_difference(paired_wellbeing.pre, - paired_wellbeing.post, - npmedian, is_paired=True) - assert median_diff == pytest.approx(4.5) - - - -def test_cohens_d_unpaired(): - import numpy as np - cohens_d = effsize.cohens_d(wellbeing.control, wellbeing.expt, - is_paired=False) - assert np.round(cohens_d, 2) == pytest.approx(0.47) - - - -def test_hedges_g_unpaired(): - import numpy as np - hedges_g = effsize.hedges_g(wellbeing.control, wellbeing.expt, - is_paired=False) - assert np.round(hedges_g, 2) == pytest.approx(0.45) - - - -def test_cohens_d_paired(): - import numpy as np - cohens_d = effsize.cohens_d(paired_wellbeing.pre, paired_wellbeing.post, - is_paired=True) - assert np.round(cohens_d, 2) == pytest.approx(0.34) - - - -def test_hedges_g_paired(): - import numpy as np - hedges_g = effsize.hedges_g(paired_wellbeing.pre, paired_wellbeing.post, - is_paired=True) - assert np.round(hedges_g, 2) == pytest.approx(0.33) - - - -def test_cliffs_delta(): - likert_delta = effsize.cliffs_delta(likert_treatment, likert_control) - assert likert_delta == pytest.approx(-0.25) - - scores_delta = effsize.cliffs_delta(b_scores, a_scores) - assert scores_delta == pytest.approx(0.65) - - - -def test_unpaired_stats(): - c = wellbeing.control - t = wellbeing.expt - - unpaired_es = TwoGroupsEffectSize(c, t, "mean_diff", is_paired=False) - - p1 = sp.stats.mannwhitneyu(c, t, alternative="two-sided").pvalue - assert unpaired_es.pvalue_mann_whitney == pytest.approx(p1) - - p2 = sp.stats.ttest_ind(c, t, nan_policy='omit').pvalue - assert unpaired_es.pvalue_students_t == pytest.approx(p2) - - p3 = sp.stats.ttest_ind(c, t, equal_var=False, nan_policy='omit').pvalue - assert unpaired_es.pvalue_welch == pytest.approx(p3) - - - -def test_paired_stats(): - before = paired_wellbeing.pre - after = paired_wellbeing.post - - paired_es = TwoGroupsEffectSize(before, after, "mean_diff", is_paired=True) - - p1 = sp.stats.ttest_rel(before, after, nan_policy='omit').pvalue - assert paired_es.pvalue_paired_students_t == pytest.approx(p1) - - p2 = sp.stats.wilcoxon(before, after).pvalue - assert paired_es.pvalue_wilcoxon == pytest.approx(p2) - - - -def test_median_diff_stats(): - c = wellbeing.control - t = wellbeing.expt - - es = TwoGroupsEffectSize(c, t, "median_diff", is_paired=False) - - p1 = sp.stats.kruskal(c, t, nan_policy='omit').pvalue - assert es.pvalue_kruskal == pytest.approx(p1) - - - -def test_ordinal_dominance(): - es = TwoGroupsEffectSize(likert_control, likert_treatment, - "cliffs_delta", is_paired=False) - - p1 = sp.stats.brunnermunzel(likert_control, likert_treatment).pvalue - assert es.pvalue_brunner_munzel == pytest.approx(p1) - - - -def test_unpaired_permutation_test(): - perm_test = PermutationTest(wellbeing.control, wellbeing.expt, - effect_size="mean_diff", - is_paired=False) - assert perm_test.pvalue == pytest.approx(0.2976) - - - -def test_paired_permutation_test(): - perm_test = PermutationTest(paired_wellbeing.pre, - paired_wellbeing.post, - effect_size="mean_diff", - is_paired=True) - assert perm_test.pvalue == pytest.approx(0.0124) - - - -def test_lqrt_unpaired(): - unpaired_dabest = Dabest(wellbeing, idx=("control", "expt"), - paired=False, id_col=None, - **dabest_default_kwargs) - lqrt_result = unpaired_dabest.mean_diff.lqrt - - p1 = lqrt.lqrtest_ind(wellbeing.control, wellbeing.expt, - equal_var=True, - random_state=12345) - - p2 = lqrt.lqrtest_ind(wellbeing.control, wellbeing.expt, - equal_var=False, - random_state=12345) - - assert lqrt_result.pvalue_lqrt_equal_var[0] == pytest.approx(p1.pvalue) - assert lqrt_result.pvalue_lqrt_unequal_var[0] == pytest.approx(p2.pvalue) - - - -def test_lqrt_paired(): - paired_dabest = Dabest(paired_wellbeing, idx=("pre", "post"), - paired=True, id_col="ID", - **dabest_default_kwargs) - lqrt_result = paired_dabest.mean_diff.lqrt - - p1 = lqrt.lqrtest_rel(paired_wellbeing.pre, paired_wellbeing.post, - random_state=12345) - - assert lqrt_result.pvalue_paired_lqrt[0] == pytest.approx(p1.pvalue) \ No newline at end of file diff --git a/dabest/tests/test_02_edge_cases.py b/dabest/tests/test_02_edge_cases.py deleted file mode 100644 index 645f01c0..00000000 --- a/dabest/tests/test_02_edge_cases.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/python -# -*-coding: utf-8 -*- -# Author: Joses Ho -# Email : joseshowh@gmail.com - - -import sys -import numpy as np -from numpy.random import PCG64, RandomState -import scipy as sp -import pytest -import pandas as pd -from .._api import load - - - -def test_unrelated_columns(N=60, random_seed=12345): - """ - Test to see if 'unrelated' columns jam up the analysis. - See Github Issue 43. - https://github.com/ACCLAB/DABEST-python/issues/44. - - Added in v0.2.5. - """ - - # rng = RandomState(MT19937(random_seed)) - rng = RandomState(PCG64(12345)) - # rng = np.random.default_rng(seed=random_seed) - - df = pd.DataFrame( - {'groups': rng.choice(['Group 1', 'Group 2', 'Group 3'], size=(N,)), - 'color' : rng.choice(['green', 'red', 'purple'], size=(N,)), - 'value': rng.random(size=(N,))}) - - df['unrelated'] = np.nan - - test = load(data=df, x='groups', y='value', - idx=['Group 1', 'Group 2']) - - md = test.mean_diff.results - - assert md.difference[0] == pytest.approx(-0.0322, abs=1e-4) - assert md.bca_low[0] == pytest.approx(-0.2279, abs=1e-4) - assert md.bca_high[0] == pytest.approx(0.1613, abs=1e-4) \ No newline at end of file diff --git a/dabest/tests/test_99_confint.py b/dabest/tests/test_99_confint.py deleted file mode 100644 index 293d0fae..00000000 --- a/dabest/tests/test_99_confint.py +++ /dev/null @@ -1,175 +0,0 @@ -#! /usr/bin/env python - -import numpy as np -from scipy.stats import norm -from scipy.stats import skewnorm -import pandas as pd -import pytest - -from .._api import load - - - -def test_paired_mean_diff_ci(): - # See Altman et al., Statistics with Confidence: - # Confidence Intervals and Statistical Guidelines (Second Edition). Wiley, 2000. - # Pg 31. - # Added in v0.2.5. - blood_pressure = {"before": [148, 142, 136, 134, 138, 140, 132, 144, - 128, 170, 162, 150, 138, 154, 126, 116], - "after" : [152, 152, 134, 148, 144, 136, 144, 150, - 146, 174, 162, 162, 146, 156, 132, 126], - "subject_id" : np.arange(1, 17)} - exercise_bp = pd.DataFrame(blood_pressure) - - - ex_bp = load(data=exercise_bp, idx=("before", "after"), - paired=True, id_col="subject_id") - paired_mean_diff = ex_bp.mean_diff.results - - assert pytest.approx(3.875) == paired_mean_diff.bca_low[0] - assert pytest.approx(9.5) == paired_mean_diff.bca_high[0] - - -# def test_paired_median_diff_ci(): -# # See Altman et al., Statistics with Confidence: -# # Confidence Intervals and Statistical Guidelines (Second Edition). Wiley, 2000. -# # Pg 31. -# endorphin = {"before": [10.6, 5.2, 8.4, 9.0, 6.6, 4.6, -# 14.1, 5.2, 4.4, 17.4, 7.2], -# "after" : [14.6, 15.6, 20.2, 20.9, 24.0, -# 25.0, 35.2, 30.2, 30.0, 46.2, 37.0], -# "subject_id" : np.arange(1, 12)} -# marathon = pd.DataFrame(endorphin) -# -# -# endorphin_marathon = load(data=marathon, idx=("before", "after"), -# paired=True, id_col="subject_id") -# paired_median_diff = endorphin_marathon.median_diff.results -# -# assert pytest.approx(10.4) == paired_median_diff.bca_low[0] -# assert pytest.approx(25.0) == paired_median_diff.bca_high[0] - - -def test_unpaired_ci(reps=30, ci=95): - # Dropped to 30 reps to save time. v0.2.5. - POPULATION_N = 10000 - SAMPLE_N = 10 - - # Create data for hedges g and cohens d. - CONTROL_MEAN = np.random.randint(1, 1000) - POP_SD = np.random.randint(1, 15) - POP_D = np.round(np.random.uniform(-2, 2, 1)[0], 2) - - TRUE_STD_DIFFERENCE = CONTROL_MEAN + (POP_D * POP_SD) - norm_sample_kwargs = dict(scale=POP_SD, size=SAMPLE_N) - c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs) - t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_STD_DIFFERENCE, **norm_sample_kwargs) - - std_diff_df = pd.DataFrame({'Control' : c1, 'Test': t1}) - - - - # Create mean_diff data - CONTROL_MEAN = np.random.randint(1, 1000) - POP_SD = np.random.randint(1, 15) - TRUE_DIFFERENCE = np.random.randint(-POP_SD*5, POP_SD*5) - - c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs) - t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_DIFFERENCE, **norm_sample_kwargs) - - mean_df = pd.DataFrame({'Control' : c1, 'Test': t1}) - - - - # Create median_diff data - MEDIAN_DIFFERENCE = np.random.randint(-5, 5) - A = np.random.randint(-7, 7) - - skew_kwargs = dict(a=A, scale=5, size=POPULATION_N) - skewpop1 = skewnorm.rvs(**skew_kwargs, loc=100) - skewpop2 = skewnorm.rvs(**skew_kwargs, loc=100+MEDIAN_DIFFERENCE) - - sample_kwargs = dict(replace=False, size=SAMPLE_N) - skewsample1 = np.random.choice(skewpop1, **sample_kwargs) - skewsample2 = np.random.choice(skewpop2, **sample_kwargs) - - median_df = pd.DataFrame({'Control' : skewsample1, 'Test': skewsample2}) - - - - # Create two populations with a 50% overlap. - CD_DIFFERENCE = np.random.randint(1, 10) - SD = np.abs(CD_DIFFERENCE) - - pop_kwargs = dict(scale=SD, size=POPULATION_N) - pop1 = norm.rvs(loc=100, **pop_kwargs) - pop2 = norm.rvs(loc=100+CD_DIFFERENCE, **pop_kwargs) - - sample_kwargs = dict(replace=False, size=SAMPLE_N) - sample1 = np.random.choice(pop1, **sample_kwargs) - sample2 = np.random.choice(pop2, **sample_kwargs) - - cd_df = pd.DataFrame({'Control' : sample1, 'Test': sample2}) - - - - # Create several CIs and see if the true population difference lies within. - error_count_cohens_d = 0 - error_count_hedges_g = 0 - error_count_mean_diff = 0 - error_count_median_diff = 0 - error_count_cliffs_delta = 0 - - for i in range(0, reps): - # print(i) # for debug. - # pick a random seed - rnd_sd = np.random.randint(0, 999999) - load_kwargs = dict(ci=ci, random_seed=rnd_sd) - - std_diff_data = load(data=std_diff_df, idx=("Control", "Test"), **load_kwargs) - cd = std_diff_data.cohens_d.results - # print("cohen's d") # for debug. - cd_low, cd_high = float(cd.bca_low), float(cd.bca_high) - if cd_low < POP_D < cd_high is False: - error_count_cohens_d += 1 - - hg = std_diff_data.hedges_g.results - # print("hedges' g") # for debug. - hg_low, hg_high = float(hg.bca_low), float(hg.bca_high) - if hg_low < POP_D < hg_high is False: - error_count_hedges_g += 1 - - - mean_diff_data = load(data=mean_df, idx=("Control", "Test"), **load_kwargs) - mean_d = mean_diff_data.mean_diff.results - # print("mean diff") # for debug. - mean_d_low, mean_d_high = float(mean_d.bca_low), float(mean_d.bca_high) - if mean_d_low < TRUE_DIFFERENCE < mean_d_high is False: - error_count_mean_diff += 1 - - - median_diff_data = load(data=median_df, idx=("Control", "Test"), - **load_kwargs) - median_d = median_diff_data.median_diff.results - # print("median diff") # for debug. - median_d_low, median_d_high = float(median_d.bca_low), float(median_d.bca_high) - if median_d_low < MEDIAN_DIFFERENCE < median_d_high is False: - error_count_median_diff += 1 - - - cd_data = load(data=cd_df, idx=("Control", "Test"), **load_kwargs) - cliffs = cd_data.cliffs_delta.results - # print("cliff's delta") # for debug. - low, high = float(cliffs.bca_low), float(cliffs.bca_high) - if low < 0.5 < high is False: - error_count_cliffs_delta += 1 - - - max_errors = int(np.ceil(reps * (100 - ci) / 100)) - - assert error_count_cohens_d <= max_errors - assert error_count_hedges_g <= max_errors - assert error_count_mean_diff <= max_errors - assert error_count_median_diff <= max_errors - assert error_count_cliffs_delta <= max_errors diff --git a/dabest/tests/utils.py b/dabest/tests/utils.py deleted file mode 100644 index 6dc5da37..00000000 --- a/dabest/tests/utils.py +++ /dev/null @@ -1,102 +0,0 @@ -def create_demo_dataset(seed=9999, N=20): - - import numpy as np - import pandas as pd - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=N) - c2 = norm.rvs(loc=3.5, scale=0.75, size=N) - c3 = norm.rvs(loc=3.25, scale=0.4, size=N) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=N) - t2 = norm.rvs(loc=2.5, scale=0.6, size=N) - t3 = norm.rvs(loc=3, scale=0.75, size=N) - t4 = norm.rvs(loc=3.5, scale=0.75, size=N) - t5 = norm.rvs(loc=3.25, scale=0.4, size=N) - t6 = norm.rvs(loc=3.25, scale=0.4, size=N) - - - # Add a `gender` column for coloring the data. - females = np.repeat('Female', N/2).tolist() - males = np.repeat('Male', N/2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, N+1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, - 'Control 2' : c2, 'Test 2' : t2, - 'Control 3' : c3, 'Test 3' : t3, - 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6, - 'Gender' : gender, 'ID' : id_col - }) - - return df - - - -def get_swarm_yspans(coll, round_result=False, decimals=12): - """ - Given a matplotlib Collection, will obtain the y spans - for the collection. Will return None if this fails. - Modified from `get_swarm_spans` in plot_tools.py. - """ - import numpy as np - _, y = np.array(coll.get_offsets()).T - try: - if round_result: - return np.around(y.min(), decimals), np.around(y.max(),decimals) - else: - return y.min(), y.max() - except ValueError: - return None - - - -# def create_dummy_dataset(seed=None, n=30, base_mean=0, -# plus_minus=5, expt_groups=7, -# scale_means=1., scale_std=1.): -# """ -# Creates a dummy dataset for plotting. -# Returns the seed used to generate the random numbers, -# the maximum possible difference between mean differences, -# and the dataset itself. -# """ -# import numpy as np -# import scipy as sp -# import pandas as pd -# -# # Set a random seed. -# if seed is None: -# random_seed = np.random.randint(low=1, high=1000, size=1)[0] -# else: -# if isinstance(seed, int): -# random_seed = seed -# else: -# raise TypeError('{} is not an integer.'.format(seed)) -# -# # Generate a set of random means -# np.random.seed(random_seed) -# MEANS = np.repeat(base_mean, expt_groups) + \ -# np.random.uniform(base_mean-plus_minus, base_mean+plus_minus, -# expt_groups) * scale_means -# SCALES = np.random.random(size=expt_groups) * scale_std -# -# max_mean_diff = np.ptp(MEANS) -# -# dataset = list() -# for i, m in enumerate(MEANS): -# pop = sp.stats.norm.rvs(loc=m, scale=SCALES[i], size=10000) -# sample = np.random.choice(pop, size=n, replace=False) -# dataset.append(sample) -# -# df = pd.DataFrame(dataset).T -# df["idcol"] = pd.Series(range(1, n+1)) -# df.columns = [str(c) for c in df.columns] -# -# return random_seed, max_mean_diff, df diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index dec31263..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = python -msphinx -SPHINXPROJ = dabest -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index a63a0ad0..00000000 --- a/docs/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Docs for DABEST-Python - -## Required Packages - -```shell -pip install --upgrade sphinx sphinx-autobuild -``` - -[Sphinx](http://www.sphinx-doc.org/en/master/index.html) is the main documentation package; [sphinx-autobuild](https://github.com/GaretJax/sphinx-autobuild) is used for hot-load development. - -## Running hot-load development - -After cloning the repo, run - -```shell -sphinx-autobuild docs/source docs/build/_html -``` - -## Creating the build - -```shell -sphinx-build -b html sourcedir builddir -``` - -## Adding custom CSS - -See the official [docs](https://docs.readthedocs.io/en/latest/guides/adding-custom-css.html), as well as this Stack Overflow [thread](https://stackoverflow.com/questions/23462494/how-to-add-custom-css-file-to-sphinx) to assign a custom CSS to the Sphinx template. - -1. Place the following line into `source/_templates/layout.html`: - -```css -{% set css_files = css_files + ['_static/css/alabaster-custom.css'] %} -``` - -2. If hot-loading doesn't reload the custom CSS, simply copy your custom CSS into the folder `_html/_static/css`. diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 8fc07e06..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,36 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=python -msphinx -) -set SOURCEDIR=source -set BUILDDIR=build -set SPHINXPROJ=dabest - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The Sphinx module was not found. Make sure you have Sphinx installed, - echo.then set the SPHINXBUILD environment variable to point to the full - echo.path of the 'sphinx-build' executable. Alternatively you may add the - echo.Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/docs/source/_archive/background.rst0 b/docs/source/_archive/background.rst0 deleted file mode 100644 index 34bb0c38..00000000 --- a/docs/source/_archive/background.rst0 +++ /dev/null @@ -1,60 +0,0 @@ -.. _background: - -========== -Background -========== - ------------------------------- -What is estimation statistics? ------------------------------- - -`Estimation statistics `_ is a simple framework that—while avoiding the pitfalls of significance testing—uses familiar statistical concepts: means, mean differences, and error bars. - ------------------------------- -Why use estimation statistics? ------------------------------- -For each of the most routine significance tests, there is an estimation replacement: - -* Unpaired Student’s t-test → Two-groups Gardner-Altman estimation plot -* Paired Student’s t-test → Paired comparison -* One-way ANOVA + multiple comparisons → Cumming multiple-groups plot -* Repeated measures ANOVA → Multi-paired comparison -* Ordered groups ANOVA → Shared-control comparison - -.. *image of all five types of plot* - -All of these plots enable you to graphically inspect the mean difference and its confidence interval. When there are multiple groups, the side-by-side plotting allows the visual comparison of effect sizes. - -.. csv-table:: Benefits of Estimation Plot - :header: " ", "Bars-and-Stars", "Boxplot & P", "Estimation Plot" - :widths: 30, 15, 15, 15 - - "Avoid false dichotomy", "✘", "✘", "✔" - "Display all observed values", "✘", "✘", "✔" - "Focus on intervention effect size", "✘", "✘", "✔" - "Visualize estimate precision", "✘", "✘", "✔" - "Show mean difference distribution", "✘", "✘", "✔" - -**1. Avoid false dichotomy.** Is there really a great difference between probabilities of 0.04999 and 0.05001? One of the many problems with significance testing is the application of an α-threshold creates the illusion of a dichotomy. In significance testing, the former is ‘significant’ and the latter is ‘non-significant.’ - -The graphical method of showing this test result with clusters of stars amplifies this false dichotomy; since the average reader is primed to look for *P* < 0.05, presenting *P* values is almost as bad. - -Estimation plots present the significance test result innocuously: as the presence or absence of a gap between the mean-difference zero line and the closest confidence interval bound. - -**2. Display all observed values.** `Bar charts `_ often show means, error, and significance stars only. `Boxplots `_ generally show just medians, quartiles, maybe a few outliers, and *P* values. For observed values, estimation plots follow best practices by presenting `each and every datapoint `_. - -Presenting all observed values means that nothing is hidden: range, normality, skew, kurtosis, outliers, bounds, modality, and sample size are all clearly visible. - -**3. Focus on intervention effect size.** Estimation estimation plots include an entirely new axis for the mean difference of two groups (or paired data), and a whole panel for the mean differences of multiple groups. This serves to draw attention to something that deserves it, the answer to the question: “What is the magnitude of the effect of the intervention?” - -**4. Visualize estimate precision.** Unlike a significance test result, the narrowness of a confidence interval gives a clear impression of effect size precision. The 95% confidence interval provides the range of the the population mean difference values that are the most plausible. - -This 95% plausible interval also serves as an 83% prediction interval for replications (`Cumming and Calin-Jageman 2016 `_), i.e. predicts future replication effect sizes (assuming no change in protocol) with 83% accuracy. - -**5. Show mean difference distribution.** The distribution of mean differences can be estimated using bootstrap resamples of the available data. As an approximation of the `Bayes posterior distribution `_, this curve allows the analyst to weigh plausibility over an effect likelihood size range. Plotting this distribution also discourages dichotomous thinking—engendered by *P* values and hard-edged confidence intervals (`Kruschke and Liddell 2017 `_)—by drawing attention to the distribution’s graded nature. - ------------------------------------------------------------- -Why are these plots named after Gardner, Altman, and Cumming? ------------------------------------------------------------- - -To our knowledge, mean difference estimation plots were first described by Martin Gardner and Douglas Altman (`Gardner and Altman 1986 `_), while the multiple-comparison design was devised by Geoff Cumming (`Cumming 2012 `_). We propose calling the two-groups plot Gardner-Altman estimation plots and the multi-group plots Cumming estimation plots. diff --git a/docs/source/_archive/dabest-r.rst0 b/docs/source/_archive/dabest-r.rst0 deleted file mode 100644 index 806de752..00000000 --- a/docs/source/_archive/dabest-r.rst0 +++ /dev/null @@ -1,197 +0,0 @@ -.. _Using `DABEST` in R: - :linenothreshold: 2 - :dedent: 4 - -=================== -Using DABEST in R -=================== - -If you haven't already obtained them, install the ``reticulate``, ``dplyr``, and -``ggplot2`` packages for R. From the R console, enter: - -.. code-block:: rout - - > install.packages(c('reticulate', 'dplyr', 'ggplot2')) - - -You will also need to have installed ``dabest`` for Python. -You can do so at the command line. - -.. code-block:: bash - - $ pip install dabest - - - -DABEST in the R Console -======================= - -Load Data ---------- - -For this demonstration, we will load the ``CO2`` dataset found in the -``datasets`` package that is bundled with R. This dataset is described -in the `official documentation`_ as follows: - - "The CO\ :sub:`2` uptake of six plants from Quebec and six plants from Mississippi - was measured at six levels of ambient CO\ :sub:`2` concentration. Half the plants - of each type were chilled overnight before the experiment was conducted." - -.. code-block:: rout - - > library(datasets) - > head(CO2) - -:: - - Plant Type Treatment conc uptake - 1 Qn1 Quebec nonchilled 95 16.0 - 2 Qn1 Quebec nonchilled 175 30.4 - 3 Qn1 Quebec nonchilled 250 34.8 - 4 Qn1 Quebec nonchilled 350 37.2 - 5 Qn1 Quebec nonchilled 500 35.3 - 6 Qn1 Quebec nonchilled 675 39.2 - - -Filter and plot the data. -------------------------- - -Select readings taken at a CO\ :sub:`2` concentration of 1000 mL/L. - -.. code-block:: rout - - > library(dplyr) - > library(ggplot2) - - > df = filter(CO2, conc == '1000') - - > ggplot(df, aes(x = Type, - y = uptake, - color = Treatment)) + - geom_point() + - ggtitle('CO2 conc = 1000 mL/L') + - xlab('City') + ylab('CO2 uptake') - -.. image:: _images/ggplot.png - -Switch to Python and use ``DABEST``. ------------------------------------- - -Load the package ``reticulate``, which will allow us to load Python packages and -to access Python objects from R. `Read more`_ about this fantastic piece of work! - - -.. code-block:: rout - - > library(reticulate) - -If you are using Anaconda, or are dealing with virtual environments, -you will need to specify the location of the specific Python environment -you are using. Use ``which python`` (Linux/OS X) or ``where python`` (Windows) -at the command line to obtain the correct path. - -.. code-block:: rout - - > use_python('/Users/your-username/anaconda3/bin/python') - -Using the ``repl_python()`` command, you can start an interactive Python session -from within R. - -.. code-block:: rout - - > repl_python() - Python 3.6.4 (/Users/whho/anaconda3/bin/python) - Reticulate 1.8 REPL -- A Python interpreter in R. - >>> - -Note the new console prompt ``>>>``. - -From the ``reticulate`` `tutorial`_ : - - Access to objects created within R chunks from Python using the r object - (e.g. r.x would access to x variable created within R from Python) - -Thus, whilst in the Python session, use ``r.`` to access -any R object you created above. (This, you have to admit, is pretty cool.) - -.. code-block:: python - - >>> import dabest - - >>> f1, results = dabest.plot(data=r.df, fig_size=(5,7), - x='Type', y='uptake', - swarm_label='CO2 uptake', - color_col='Treatment', - idx=['Quebec', 'Mississippi']) - >>> f1.savefig('dabest-plot-CO2.png', bbox_inches='tight') - >>> exit - > - -.. image:: _images/dabest-plot-CO2.png - -A few things to note: - -1. It's best to save the generated ``dabest`` plot from within the Python session. - -2. You can quickly exit the Python session with (who would have guessed) ``exit``. - - -Now, you are back in the R session. All the objects generated in Python are -accessible via the ``py`` object; use the ``$`` operator to access named variables. - -.. code-block:: rout - - > py$results - -:: - - reference_group experimental_group stat_summary bca_ci_low bca_ci_high ci - 1 Quebec Mississippi -16.83333 -22.98333 -10.9 95 - is_difference is_paired pvalue_2samp_ind_ttest pvalue_mann_whitney - 1 TRUE FALSE 0.0005511919 0.005074868 - -Because ``results`` is a Python ``pandas`` object, ``py$results`` is an -R ``data.frame``; its attributes can be accessed easily via the ``$`` -operator. - -.. code-block:: rout - - > py_results = py$results - > mean_diff = py_results$stat_summary - > ci_low = py_results$bca_ci_low - > ci_high = py_results$bca_ci_high - -Print results, with all numerical values formatted to 2 decimal places. - -.. code-block:: rout - - > sprintf("Mean Difference = %.2f [95CI %.2f, %.2f]", - mean_diff, ci_low, ci_high) - -:: - - [1] "Mean Difference = -16.83 [95CI -22.98, -10.90]" - - -DABEST in R Markdown -==================== - -R Markdown is able to run code from different languages `in the same document`_. -From that last link: - - To process a code chunk using an alternate language engine, - replace the r at the start of your chunk declaration - with the name of the language. - -A minimal example is shown below, and can be downloaded here as an :download:`R Markdown file <_static/reticulate_tutorial.Rmd>`. - -.. image:: _images/dabest-in-r-markdown.png - - -.. _official documentation: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/zCO2.html - -.. _Read more: https://rstudio.github.io/reticulate/#importing-python-modules - -.. _tutorial: https://rstudio.github.io/reticulate/#python-in-r-markdown - -.. _in the same document: https://rmarkdown.rstudio.com/lesson-5.html diff --git a/docs/source/_images/showpiece.png b/docs/source/_images/showpiece.png deleted file mode 100644 index bd8daa44..00000000 Binary files a/docs/source/_images/showpiece.png and /dev/null 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-PyPI version -

diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html deleted file mode 100644 index b052dda1..00000000 --- a/docs/source/_templates/layout.html +++ /dev/null @@ -1,16 +0,0 @@ -{% extends "!layout.html" %} -{% set css_files = css_files + ['_static/css/custom.css'] %} - -{%- block extrahead %} -{{ super() }} - - - - -{% endblock %} diff --git a/docs/source/about.rst b/docs/source/about.rst deleted file mode 100644 index de38d8ed..00000000 --- a/docs/source/about.rst +++ /dev/null @@ -1,63 +0,0 @@ -.. _about: - -===== -About -===== - - -Authors --------- -DABEST is written in Python by `Joses W. Ho `_, with design and input from `Adam Claridge-Chang `_ and other `lab members `__. - -To find out more about the authors’ research, please visit the `Claridge-Chang lab webpage `_. - -Contributors ------------- - -- DizietAsahi (`DizietAsahi `_) with `PR #86 `_: Fix bugs in slopegraph and reference line keyword parsing. - -- Adam Li (`@adam2392 `_) with `PR #85 `_: Implement `Lq-Likelihood-Ratio-Type Test `_ in statistical output. - -- Mason Malone (`@MasonM `_) with `PR #30 `_: Fix plot error when effect size is 0. - -- Matthew Edwards (`@mje-nz `_) with `PR #71 `_: Specify dependencies correctly in ``setup.py``. - -- Adam Nekimken (`@anekimken `_) with `PR #73 `_: Implement inset axes so estimation plots can be plotted on a pre-determined :py:mod:`matplotlib` :py:class:`Axes` object. - - -Typography ----------- - -This documentation uses `Spectral `_ for the body text, `Merriweather Sans `_ for the side bar and titles, and `IBM Plex Mono `_ for the monospace code font. - - -License -------- - -The DABEST package in Python is licenced under the `BSD 3-clause Clear License `_. - -Copyright (c) 2016-2020, Joses W. Ho -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted (subject to the limitations in the disclaimer -below) provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. - - * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. - -NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR -BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER -IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/source/api.rst b/docs/source/api.rst deleted file mode 100644 index 3f197048..00000000 --- a/docs/source/api.rst +++ /dev/null @@ -1,36 +0,0 @@ -.. _api: - -.. currentmodule:: dabest - -API -=== - -Loading Data -------------- - -.. autofunction:: load - - -Computing Effect Sizes ----------------------- - -.. autoclass:: dabest._classes.Dabest - :members: mean_diff, median_diff, cohens_d, hedges_g, cliffs_delta - :member-order: bysource - -.. .. autoclass:: dabest._classes.TwoGroupsEffectSize - - -Plotting Data -------------- - -.. autoclass:: dabest._classes.EffectSizeDataFrame - :members: plot, lqrt - :member-order: bysource - - - -Permutation Tests ------------------ - -.. autoclass:: dabest._classes.PermutationTest \ No newline at end of file diff --git a/docs/source/bootstraps.rst b/docs/source/bootstraps.rst deleted file mode 100644 index 58625d36..00000000 --- a/docs/source/bootstraps.rst +++ /dev/null @@ -1,104 +0,0 @@ -.. _Bootstrap Confidence Intervals: - - -============================== -Bootstrap Confidence Intervals -============================== - -Sampling from Populations -------------------------- -In a typical scientific experiment, we are interested in two populations -(Control and Test), and whether there is a difference between their means -(µ :sub:`Test` - µ :sub:`Control`). - - -.. image:: _images/bootstrap-1.png - -We go about this by collecting observations from the control population, and -from the test population. - - -.. image:: _images/bootstrap-2.png - -We can easily compute the mean difference in our observed samples. This is our -estimate of the population effect size that we are interested in. - -**But how do we obtain a measure of precision and confidence about our estimate? -Can we get a sense of how it relates to the population mean difference?** - -The bootstrap confidence interval ---------------------------------- - -We want to obtain a 95% confidence interval (95% CI) around the our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time. - -In other words, if we repeated our experiment 100 times, gathering 100 independent sets of observations, and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population. - -We can calculate the 95% CI of the mean difference with `bootstrap resampling `__. - - -The bootstrap in action -~~~~~~~~~~~~~~~~~~~~~~~ - -The bootstrap [1]_ is a simple but powerful technique. It was `first described `__ by `Bradley Efron `__. - -It creates multiple *resamples* (with replacement) from a single set of -observations, and computes the effect size of interest on each of these -resamples. The bootstrap resamples of the effect size can then be used to -determine the 95% CI. - -With computers, we can perform 5000 resamples very easily. - - -.. image:: _images/bootstrap-3.png - - -The resampling distribution of the difference in means approaches a normal -distribution. This is due to the `Central Limit Theorem `__: a large number of -independent random samples will approach a normal distribution even if the -underlying population is not normally distributed. - -Bootstrap resampling gives us two important benefits: - -1. *Non-parametric statistical analysis.* There is no need to assume that our -observations, or the underlying populations, are normally distributed. Thanks to -the Central Limit Theorem, the resampling distribution of the effect size will -approach a normality. - -2. *Easy construction of the 95% CI from the resampling distribution.* For 1000 -bootstrap resamples of the mean difference, one can use the 25th value and the -975th value of the ranked differences as boundaries of the 95% confidence -interval. (This captures the central 95% of the distribution.) Such an interval -construction is known as a *percentile interval*. - - -Adjusting for asymmetrical resampling distributions ---------------------------------------------------- - -While resampling distributions of the difference in means often have a normal -distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron -developed the `bias-corrected and accelerated bootstrap -`__ (BCa -bootstrap) to account for the skew, and still obtain the central 95% of the -distribution. - -DABEST applies the BCa correction to the resampling bootstrap distributions of -the effect size. - -.. image:: _images/bootstrap-4.png - - -Estimation plots incorporate bootstrap resampling -------------------------------------------------- - -The estimation plot produced by DABEST presents the rawdata and the bootstrap -confidence interval of the effect size (the difference in means) side-by-side as -a single integrated plot. - - -.. image:: _images/bootstrap-5.png - - -It thus tightly couples visual presentation of the raw data with an indication of the population mean difference, and its confidence interval. - - -.. [1] The name is derived from the saying “`pull oneself by one’s bootstraps `__”, often used as an exhortation to achieve success without external help. diff --git a/docs/source/citation.rst b/docs/source/citation.rst deleted file mode 100644 index 89d7615c..00000000 --- a/docs/source/citation.rst +++ /dev/null @@ -1,17 +0,0 @@ -.. _Citing DABEST: - - -============= -Citing DABEST -============= - -If your publication features a graphic generated with this software library, please cite the following publication. - -**Moving beyond P values: Everyday data analysis with estimation plots** -Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang - -`Nature Methods` 2019, 1548-7105. `doi:10.1038/s41592-019-0470-3 `__ - -`Free-to-view PDF `__ - -`Paywalled publisher site `__ diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 70037856..00000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# -# dabest documentation build configuration file, created by -# sphinx-quickstart on Tue Dec 12 13:45:30 2017. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# This value selects what content will be inserted into the main body of an -# autoclass directive. The possible values are: -# -# "class" Only the class’ docstring is inserted. This is the default. You can -# still document __init__ as a separate method using automethod or the members -# option to autoclass. -# -# "both" Both the class’ and the __init__ method’s docstring are concatenated and -# inserted. -# -# "init" Only the __init__ method’s docstring is inserted. -# -autoclass_content = "init" - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', - 'sphinx.ext.mathjax', 'sphinx.ext.githubpages', 'sphinx.ext.intersphinx'] - -# Add mappings -intersphinx_mapping = { - 'seaborn': ('https://seaborn.pydata.org', None), -} - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = 'dabest' -copyright = '2017-2020, Joses W. Ho' -author = 'Joses W. Ho' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = '0.3' -# The full version, including alpha/beta/rc tags. -release = '0.3.1' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This patterns also effect to html_static_path and html_extra_path -exclude_patterns = [] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. -# Built-in themes: alabaster, classic, sphinxdoc, scrolls, agogo, traditional, -# nature, haiku, pyramid, bizstyle -html_theme = 'alabaster' - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# This is required for the alabaster theme -# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars -html_sidebars = { - '**': [ - 'about.html', - 'badges.html', - 'navigation.html', - 'relations.html', # needs 'show_related': True theme option to display - 'searchbox.html', - 'donate.html', - ] -} - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -html_theme_options = { - # 'font_family': 'Georgia', - # 'head_font_family': 'Palatino', - 'font_size': '16px', - - 'code_font_size': '14px', - - 'github_banner': True, - 'github_button': True, - 'github_type': 'star', - 'github_user': 'ACCLAB', - 'github_repo': 'DABEST-python', - -} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static/css'] - - - - -# -- Options for HTMLHelp output ------------------------------------------ - -# Output file base name for HTML help builder. -htmlhelp_basename = 'dabestdoc' - - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'dabest.tex', 'dabest Documentation', - 'Joses W. Ho', 'manual'), -] - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'dabest', 'dabest Documentation', - [author], 1) -] - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'dabest', 'dabest Documentation', - author, 'dabest', - 'Data Analysis and Visualization using Bootstrap-Coupled Estimation.', - 'Miscellaneous'), -] - - - -# -- Options for Epub output ---------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project -epub_author = author -epub_publisher = author -epub_copyright = copyright - -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# -# epub_identifier = '' - -# A unique identification for the text. -# -# epub_uid = '' - -# A list of files that should not be packed into the epub file. -epub_exclude_files = ['search.html'] - -def setup(app): - app.add_stylesheet('css/alabaster-custom.css') diff --git a/docs/source/getting-started.rst b/docs/source/getting-started.rst deleted file mode 100644 index 2f872e81..00000000 --- a/docs/source/getting-started.rst +++ /dev/null @@ -1,63 +0,0 @@ -.. _Getting Started: - -=============== -Getting Started -=============== - - -Requirements ------------- - -Python 3.8 is strongly recommended. DABEST has also been tested with Python 3.6 and 3.7. - -In addition, the following packages are also required (listed with their minimal versions): - -* `numpy 1.19 `_ -* `scipy 1.5 `_ -* `matplotlib 3.3 `_ -* `pandas 1.1 `_ -* `seaborn 0.11 `_ -* `lqrt 0.3 `_ - -To obtain these package dependencies easily, it is highly recommended to download the `Anaconda `_ distribution of Python. - - -Installation ------------- - -1. Using ``pip`` - -At the command line, run - -.. code-block:: console - - $ pip install dabest - -2. Using Github - -Clone the `DABEST-python repo `_ locally (see instructions `here `_). - -Then, navigate to the cloned repo in the command line and run - -.. code-block:: console - - $ pip install . - - - -Testing -------- - -To test DABEST, you will need to install `pytest `_. - -Run ``pytest`` in the root directory of the source distribution. This runs the test suite in ``dabest/tests`` folder. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected. - - -Bugs ----- -Please report any bugs on the `Github issue tracker `_ for DABEST-python. - - -Contributing ------------- -All contributions are welcome. Please fork the `Github repo `_ and open a pull request. diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index b2b88c27..00000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,62 +0,0 @@ -.. dabest documentation master file. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive - -====== -DABEST -====== - ------------------------------------------------ -Data Analysis with Bootstrap-coupled ESTimation ------------------------------------------------ - -Analyze your data with estimation statistics! ---------------------------------------------- - -.. image:: _images/showpiece.png - - -News ----- -October 2020: - - v0.3.1 released. The minimal versions of dependencies have been upgraded. Also, the minimal version of Python required is now 3.5. - -January 2020: - - v0.3.0 released. Approximate permutation tests have been added, and are now the default p-values reported in the textual output. The LqRT tests were also refactored to a user-callable property. For more information, see the :doc:`release-notes`. - -December 2019: - - v0.2.8 released. This release adds the `Lq-Likelihood-Ratio-Type Test `_ in the statistical output, and also a bugfix for slopegraph and reference line keyword parsing. - -October 2019: - - v0.2.7 released. A minor bugfix in the handling of wide datasets with unequal Ns in each group. - - v0.2.6 released. This release has one new feature (plotting of estimation plot inside any :py:mod:`matplotlib` :py:class:`Axes`; see the section on :ref:`inset plot` in the :doc:`tutorial`). There are also two bug patches for the handling of bootstrap plotting, and of dependency installation. - -September 2019: - - v0.2.5 released. This release addresses two feature requests, and also patches two bugs: one affecting the paired difference CIs, and one involving NaNs in unused/irrelevant columns. - -May 2019: - - v0.2.4 released. This is a patch for a set of bugs that mis-aligned Gardner-Altman plots, and also adds the capability to tweak the x-position of the Tufte gapped lines. - - - v0.2.3 released. This is a fix for a bug that did not properly handle x-columns which were pandas Categorical objects. - -April 2019: - - v0.2.2 released. This is a minor bugfix that addressed an issue for an edge case where the mean or median difference was exactly zero. - -March 2019: - - v0.2.1 released. This is a minor bugfix that addressed an issue in gapped line plotting. - - v0.2.0 released. This is a major update that makes several breaking changes to the API. - -Contents --------- - -.. toctree:: - :maxdepth: 1 - - robust-beautiful - bootstraps - getting-started - tutorial - release-notes - api - about - citation diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst deleted file mode 100644 index 1cad9301..00000000 --- a/docs/source/release-notes.rst +++ /dev/null @@ -1,221 +0,0 @@ -.. _Release Notes: - -============= -Release Notes -============= - -v0.3.1 ------- - -This release updates the minimal Python version to 3.6 (as `Python 3.5 is already an "end-of-life" release as of September 2020 `_), and also updates the package requirements to: - - :py:mod:`numpy`: 0.19 - - :py:mod:`matplotlib`: 3.3 - - :py:mod:`scipy`: 1.5 - - :py:mod:`pandas`: 1.1 - - :py:mod:`seaborn`: 0.11 - - :py:mod:`lqrt`: 0.3 - -All users are strongly encouraged to update. - -v0.3.0 ------- -This is a new major release that refactors the statistical test output. All users are strongly encouraged to update to this release. - -Main Changes: - - the Lq-Likelihood Ratio-Type test results are now computed only when the user calls the `lqrt` property of the `dabest_object.effect_size` :py:class:`EffectSizeDataFrame` object. This refactor was done to mitigate the lengthy computation time the test took. See Issues `#94 `_ and `#91 `_. - - The default hypothesis test reported by calling the `dabest_object.effect_size` :py:class:`EffectSizeDataFrame` object is now the `non-parametric two-sided permutation t-test `_. Conceptually, this hypothesis test mirrors the usage of the bootstrap (to obtain the 95% confidence intervals). Read more at :doc:`tutorial`. See Issue `#92 `_ and PR `#93 `_. - - The minimum version of :py:mod:`numpy` is now v0.17, which has an updated method of generating random samples. The resampling code used in :py:mod:`dabest` has thus been updated as well. - - -v0.2.8 ------- - -This release fixes minor bugs, and implements a new statistical test. - -Feature Additions: - - Implement `Lq-Likelihood-Ratio-Type Test `_ in statistical output with `PR #85 `_; thanks to Adam Li (`@adam2392 `_). - -Bug-fixes: - - Fix bugs in slopegraph and reference line keyword parsing with `PR #86 `_; thanks to DizietAsahi (`DizietAsahi `_). - - - -v0.2.7 ------- - -Bug-fixes: - - Bug affecting display of Tufte gapped lines in Cumming plots if the supplied :py:mod:`pandas` :py:class:`DataFrame` was in 'wide' format, but did not have equal number of Ns in the groups. (`Issue #79 `_) - - -v0.2.6 ------- - -Feature additions: - - It is now possible to specify a pre-determined :py:mod:`matplotlib` :py:class:`Axes` to create the estimation plot in. See :ref:`inset plot` in the :doc:`tutorial` (`Pull request #73 `_; thanks to Adam Nekimken (`@anekimken `_). - - - - -Bug-fixes: - - Ensure all dependencies are installed along with DABEST. (`Pull request #71 `_; thanks to Matthew Edwards (`@mje-nz `_). - - Handle infinities in bootstraps during plotting. (`Issue #72 `_, `Pull request #74 `_) - -v0.2.5 ------- - -Feature additions: - - Adding Ns of each group to the results DataFrame. (`Issue #45 `_) - - Auto-labelling the swarmplot rawdata axes y-label. (`Issue #51 `_) - -Bug-fixes: - - Bug affecting calculation of paired difference confidence intervals. (`Issue #48 in ACCLAB/dabestr `_) - - NaNs in unused/unrelated columns would result in null results (`Issue #44 `_) - - -v0.2.4 ------- - -This release fixes the following issues: - - Misalignment of Gardner-Altman plots when the dataset loaded is wide, but has NaNs in a column. (`Issue #40 `_) - - Misalignment of Hedges' g Gardner Altman plots (Also Issue #40). - - Add ``groups_summaries_offset`` argument for better control over gapped Tufte line plotting. The default offset is now set at 0.1 as well. (`Issue #35 `_ - -v0.2.3 ------- - -This release fixes a bug that did not handle when the supplied ``x`` was a :py:mod:`pandas` :py:class:`Categorical` object, but the ``idx`` did not include all the original categories. - - -v0.2.2 ------- - -This release fixes a `bug `_ that has a mean difference or median difference of exactly 0. (`Pull request #73 `_; thanks to Mason Malone (`@MasonM `_). - - -v0.2.1 ------- - -This release fixes a bug that misplotted the gapped summary lines in Cumming plots when the *x*-variable was a :py:mod:`pandas` :py:class:`Categorical` object. - - -v0.2.0 ------- - -We have redesigned the interface from the ground up. This allows speed and flexibility to compute different effect sizes (including Cohen's *d*, Hedges' *g*, and Cliff's delta). Statistical arguments are now parsed differently from graphical arguments. - -In short, any code relying on v0.1.x will **not work with v0.2.0, and must be upgraded.** - -Now, every analysis session begins with ``dabest.load()``. - -.. code-block:: python - :linenos: - - my_data = dabest.load(my_dataframe, idx=("Control", "Test")) - -This creates a :py:class:`Dabest` object with effect sizes as instances. - -.. code-block:: python - :linenos: - - my_data.mean_diff - -which prints out: - -.. parsed-literal:: - - DABEST v0.2.0 - ============= - - Good afternoon! - The current time is Mon Mar 4 17:03:29 2019. - - The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774]. - - 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated. - The p-value(s) reported are the likelihood(s) of observing the effect size(s), - if the null hypothesis of zero difference is true. - -The following are valid effect sizes: - -.. code-block:: python - :linenos: - - my_data.mean_diff - my_data.median_diff - my_data.cohens_d - my_data.hedges_g - my_data.cliffs_delta - -To produce an estimation plot, each effect size instance has a ``plot()`` method. - -.. code-block:: python - :linenos: - - my_data.mean_diff.plot() - -See the :doc:`tutorial` and :doc:`api` for more details, including keyword options for the ``load()`` and ``plot()`` methods. - - -v0.1.7 ------- - -The keyword ``cumming_vertical_spacing`` has been added to tweak the vertical spacing between the rawdata swarm axes and the contrast axes in Cumming estimation plots. - -v0.1.6 ------- - -Several keywords have been added to allow more fine-grained control over a selection of plot elements. - -* ``swarm_dotsize`` -* ``difference_dotsize`` -* ``ci_linewidth`` -* ``summary_linewidth`` - -The new keyword ``context`` allows you to set the plotting context as defined by seaborn's `plotting_context() `_ . - -Now, if ``paired=True``, you will need to supply an ``id_col``, which is a column in the DataFrame which specifies which sample the datapoint belongs to. See the :doc:`tutorial` for more details. - - -v0.1.5 ------- -Fix bug that wasn't updating the seaborn version upon setup and install. - - -v0.1.4 ------- -Update dependencies to - -* numpy 1.15 -* scipy 1.1 -* matplotlib 2.2 -* seaborn 0.9 - -Aesthetic changes - -* add ``tick_length`` and ``tick_pad`` arguments to allow tweaking of the axes tick lengths, and padding of the tick labels, respectively. - - -v0.1.3 ------- -Update dependencies to - -* pandas v0.23 - -Bugfixes - -* fix bug that did not label ``swarm_label`` if raw data was in tidy form -* fix bug that did not dropnans for unpaired diff - - -v0.1.2 ------- -Update dependencies to - -* numpy v1.13 -* scipy v1.0 -* pandas v0.22 -* seaborn v0.8 - - -v0.1.1 ------- -`Update LICENSE to BSD-3 Clear. `_ diff --git a/docs/source/robust-beautiful.rst b/docs/source/robust-beautiful.rst deleted file mode 100644 index 4e853b19..00000000 --- a/docs/source/robust-beautiful.rst +++ /dev/null @@ -1,227 +0,0 @@ -.. _Robust and Beautiful Visualization: - -.. role:: raw-html(raw) - :format: html - - -============================================== -Robust and Beautiful Statistical Visualization -============================================== - -Current plots do not work -------------------------- - -What is data visualization? Battle-Baptiste and Rusert (2018) give a -cogent and compelling definition: - - [Data visualization is] the rendering of information in a visual - format to help communicate data while also generating new patterns - and knowledge through the act of visualization itself. [1]_ - -Sadly, too many figures and visualizations in modern academic -publications seemingly fail to "generate new patterns and knowledge -through the act of visualization itself". Here, we propose a solution: -*the estimation plot*. - - - -The barplot conceals the underlying shape -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -By only displaying the mean and standard deviation, barplots do not -accurately represent the underlying distribution of the data. - - -.. image:: _images/robust_5_1.png - - -In the above figure, four different samples with wildly different -distributions—as seen in the swarmplot on the left panel—look exactly -the same when visualized with a barplot on the right panel. (You can -download the :download:`dataset <_static/four_samples.csv>` to see for yourself.) - -We're not the first ones (see -`this `__, -`this `__, -or -`that `__) -to point out the barplot's fatal flaws. Indeed, it is both sobering and -fascinating to realise that the barplot is a `17th century -invention `__ initially -used to compare single values, not to compare summarized and aggregated -data. - -The boxplot does not convey sample size -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Boxplots are another widely used visualization tool. They arguably do -include more information for each sample (medians, quartiles, maxima, -minima, and outliers), but they do not convey to the viewer the size of -each sample. - - - -.. image:: _images/robust_7_1.png - - -The figure above visualizes the same four samples as a swarmplot (left -panel) and as a boxplot. If we did not label the x-axis with the sample -size, it would be impossible to definitively distinguish the sample with -5 obesrvations from the sample with 50. - -Even if the world gets rid of barplots and boxplots, the problems -plaguing statistical practices will remain unsolved. Null-hypothesis -significance testing—the dominant statistical paradigm in basic -research—does not indicate the **effect size**, or its **confidence -interval**. - - -Introducing the Estimation Plot -------------------------------- - -.. image:: _images/robust_9_0.png - - -Shown above is a Gardner-Altman estimation plot. (The plot draws its name from -`Martin J. Gardner -`__ -and `Douglas Altman `__, who are -credited with `creating the design -`__ in 1986). - -This plot has two key features: - - 1. It presents all datapoints as a *swarmplot*, which orders each point to - display the underlying distribution. - - 2. It presents the effect size as a *bootstrap 95% confidence interval* (95% CI) - on a separate but aligned axes. where the effect size is displayed to the right - of the war data, and the mean of the test group is aligned with the effect size. - -*Thus, estimation plots are robust, beautiful, and convey important statistical -information elegantly and efficiently.* - -An estimation plot obtains and displays the 95% CI through nonparametric -bootstrap resampling. This enables visualization of the confidence interval as -a graded sampling distribution. - -This is one important difference between estimation plots created by DABEST, and -the original Gardner-Altman design. Here, the 95% CI is computed through -parametric methods, and displayed as a vertical error bar. - -Read more about this technique at :ref:`Bootstrap Confidence Intervals`. - -Introducing Estimation Statistics -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Estimation plots emerge from *estimation statistics*, a simple -`framework `__ that avoids the -`pitfalls of significance -testing `__. It focuses on -the effect sizes of one’s experiment/interventions, and uses familiar -statistical concepts: means, mean differences, and error bars. - -Significance testing calculates the probability (the *P* value) that the -experimental data would be observed, if the intervention did not produce -a change in the metric measured (i.e. the null hypothesis). This leads -analysts to apply a false dichotomy on the experimental intervention. - -Estimation statistics, on the other hand, focuses on the magnitude of -the effect (the effect size) and its precision. This encourages analysts -to gain a deeper understanding of the metrics used, and how they relate -to the natural processes being studied. - -An Estimation Plot For Every Experimental Design ------------------------------------------------- - -For each of the most routine significance tests, there is an estimation -replacement: - -Unpaired Student’s t-test → Two-group estimation plot -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: _images/robust_12_0.png - - -Paired Student’s t-test → Paired estimation plot -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The Gardner-Altman estimation plot can also display effect sizes for -repeated measures (aka a paired experimental design) using a `Tufte -slopegraph `__ instead of a -swarmplot. - - - -.. image:: _images/robust_14_0.png - - -One-way ANOVA + multiple comparisons → Multi two-group estimation plot -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -For comparisons between 3 or more groups that typically employ analysis -of variance (ANOVA) methods, one can use the `Cumming estimation -plot `__, -named after `Geoff -Cumming `__, and draws its -design heavily from his 2012 textbook `Understanding the New -Statistics `__. -This estimation plot design can be considered a variant of the -Gardner-Altman plot. - - -.. image:: _images/robust_16_0.png - - -The effect size and 95% CIs are still plotted a separate axes, but -unlike the Gardner-Altman plot, this axes is positioned beneath the raw -data. - -Such a design frees up visual space in the upper panel, allowing the -display of summary measurements (mean ± standard deviation) for each -group. These are shown as gapped lines to the right of each group. The -mean of each group is indicated as a gap in the line, adhering to Edward -Tufte’s dictum to `keep the data-ink ratio -low `__. - -Repeated measures ANOVA → Multi paired estimation plot -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: _images/robust_19_0.png - - -Ordered groups ANOVA → Shared-control estimation plot -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - -.. image:: _images/robust_21_0.png - - -Estimation Plots: The Way Forward ---------------------------------- - -In summary, estimation plots offer five key benefits relative to -conventional plots: - -+--------------------------------------+-----------+-----------+-------------------+ -|   | Barplot | Boxplot | Estimation Plot | -+======================================+===========+===========+===================+ -| Displays all observed values | ✘ | ✘ | ✅ | -+--------------------------------------+-----------+-----------+-------------------+ -| Avoids false dichotomy | ✘ | ✘ | ✅ | -+--------------------------------------+-----------+-----------+-------------------+ -| Focusses on effect size | ✘ | ✘ | ✅ | -+--------------------------------------+-----------+-----------+-------------------+ -| Visualizes effect size precision | ✘ | ✘ | ✅ | -+--------------------------------------+-----------+-----------+-------------------+ -| Shows mean difference distribution | ✘ | ✘ | ✅ | -+--------------------------------------+-----------+-----------+-------------------+ - -You can create estimation plots using the DABEST (Data Analysis with -Bootstrap Estimation) packages, which are available in -`Matlab `__, -`Python `__, and -`R `__. - - -.. [1] `W. E. B. Du Bois’s Data Portraits: Visualizing Black America `__. Edited by Whitney Battle-Baptiste and Britt Rusert, Princeton Architectural Press, 2018 diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst deleted file mode 100644 index 62b63963..00000000 --- a/docs/source/tutorial.rst +++ /dev/null @@ -1,1406 +0,0 @@ -.. _Tutorial: - -======== -Tutorial -======== - - -Load Libraries --------------- - -.. code-block:: python3 - :linenos: - - - import numpy as np - import pandas as pd - import dabest - - print("We're using DABEST v{}".format(dabest.__version__)) - - -.. parsed-literal:: - - We're using DABEST v0.3.1 - - -Create dataset for demo ------------------------ - -Here, we create a dataset to illustrate how ``dabest`` functions. In -this dataset, each column corresponds to a group of observations. - -.. code-block:: python3 - :linenos: - - - from scipy.stats import norm # Used in generation of populations. - - np.random.seed(9999) # Fix the seed so the results are replicable. - # pop_size = 10000 # Size of each population. - Ns = 20 # The number of samples taken from each population - - # Create samples - c1 = norm.rvs(loc=3, scale=0.4, size=Ns) - c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns) - c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns) - - t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns) - t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns) - t3 = norm.rvs(loc=3, scale=0.75, size=Ns) - t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns) - t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns) - t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns) - - - # Add a `gender` column for coloring the data. - females = np.repeat('Female', Ns/2).tolist() - males = np.repeat('Male', Ns/2).tolist() - gender = females + males - - # Add an `id` column for paired data plotting. - id_col = pd.Series(range(1, Ns+1)) - - # Combine samples and gender into a DataFrame. - df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, - 'Control 2' : c2, 'Test 2' : t2, - 'Control 3' : c3, 'Test 3' : t3, - 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6, - 'Gender' : gender, 'ID' : id_col - }) - -Note that we have 9 groups (3 Control samples and 6 Test samples). Our -dataset also has a non-numerical column indicating gender, and another -column indicating the identity of each observation. - -This is known as a ‘wide’ dataset. See this -`writeup `__ -for more details. - -.. code-block:: python3 - :linenos: - - - df.head() - - - - -.. raw:: html - -
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Control 1Test 1Control 2Test 2Control 3Test 3Test 4Test 5Test 6GenderID
02.7939843.4208753.3246611.7074673.8169401.7965814.4400502.9372843.486127Female1
13.2367593.4679723.6851861.1218463.7503583.9445663.7234942.8370622.338094Female2
23.0191494.3771795.6168913.3013812.9453972.8321883.2140143.1119503.270897Female3
32.8046384.5647802.7731522.5340183.5751793.0482674.9682783.7433783.151188Female4
42.8580193.2200582.5503612.7963653.6921383.2765752.6621042.9773412.328601Female5
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- - - -Loading Data ------------- - -Before we create estimation plots and obtain confidence intervals for -our effect sizes, we need to load the data and the relevant groups. - -We simply supply the DataFrame to ``dabest.load()``. We also must supply -the two groups you want to compare in the ``idx`` argument as a tuple or -list. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), resamples=5000) - -Calling this ``Dabest`` object gives you a gentle greeting, as well as -the comparisons that can be computed. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired - - - - -.. parsed-literal:: - - DABEST v0.3.1 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:44 2020. - - Effect size(s) with 95% confidence intervals will be computed for: - 1. Test 1 minus Control 1 - - 5000 resamples will be used to generate the effect size bootstraps. - - - -Changing statistical parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If the dataset contains paired data (ie. repeated observations), specify -this with the ``paired`` keyword. You must also pass a column in the -dataset that indicates the identity of each observation, using the -``id_col`` keyword. - -.. code-block:: python3 - :linenos: - - - two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"), - paired=True, id_col="ID") - -.. code-block:: python3 - :linenos: - - - two_groups_paired - - - - -.. parsed-literal:: - - DABEST v0.3.1 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:44 2020. - - Paired effect size(s) with 95% confidence intervals will be computed for: - 1. Test 1 minus Control 1 - - 5000 resamples will be used to generate the effect size bootstraps. - - - -You can also change the width of the confidence interval that will be -produced. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired_ci90 = dabest.load(df, idx=("Control 1", "Test 1"), ci=90) - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired_ci90 - - - - -.. parsed-literal:: - - DABEST v0.3.1 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:44 2020. - - Effect size(s) with 90% confidence intervals will be computed for: - 1. Test 1 minus Control 1 - - 5000 resamples will be used to generate the effect size bootstraps. - - - -Effect sizes ------------- - -``dabest`` now features a range of effect sizes: - - the mean difference (``mean_diff``) - - the median difference (``median_diff``) - - `Cohen’s d `__ (``cohens_d``) - - `Hedges’ g `__ (``hedges_g``) - - `Cliff’s delta `__ (``cliffs_delta``) - -Each of these are attributes of the ``Dabest`` object. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.mean_diff - - - - -.. parsed-literal:: - - DABEST v0.3.1 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:44 2020. - - The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768]. - The p-value of the two-sided permutation t-test is 0.001. - - 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated. - The p-value(s) reported are the likelihood(s) of observing the effect size(s), - if the null hypothesis of zero difference is true. - For each p-value, 5000 reshuffles of the control and test labels were performed. - - To get the results of all valid statistical tests, use `.mean_diff.statistical_tests` - - - -For each comparison, the type of effect size is reported (here, it’s the -“unpaired mean difference”). The confidence interval is reported as: -[*confidenceIntervalWidth* *LowerBound*, *UpperBound*] - -This confidence interval is generated through bootstrap resampling. See -:doc:`bootstraps` for more details. - -Since v0.3.0, DABEST will report the p-value of the `non-parametric two-sided approximate permutation t-test `__. This is also known as the Monte Carlo permutation test. - -For unpaired comparisons, the p-values and test statistics of `Welch's t test `__, `Student's t test `__, and `Mann-Whitney U test `__ can be found in addition. For paired comparisons, the p-values and test statistics of the `paired Student's t `__ and `Wilcoxon `__ tests are presented. - -.. code-block:: python3 - :linenos: - - - pd.options.display.max_columns = 50 - two_groups_unpaired.mean_diff.results - - - - -.. raw:: html - -
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0Control 1Test 12020mean differenceFalse0.48029950.2208690.767721(140, 4889)0.2156970.761716(125, 4875)0.0015000[-0.157303571150468, -0.025932185794146356, 0....5000123450.002094-3.3088060.002057-3.3088060.00162583.0
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- - - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.mean_diff.statistical_tests - - - - -.. raw:: html - -
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highpvalue_permutationpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020mean differenceFalse0.48029950.2208690.7677210.0010.002094-3.3088060.002057-3.3088060.00162583.0
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- - - -Let’s compute the Hedges’ *g* for our comparison. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.hedges_g - - - - -.. parsed-literal:: - - DABEST v0.3.0 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:46 2020. - - The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.349, 1.62]. - The p-value of the two-sided permutation t-test is 0.001. - - 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated. - The p-value(s) reported are the likelihood(s) of observing the effect size(s), - if the null hypothesis of zero difference is true. - For each p-value, 5000 reshuffles of the control and test labels were performed. - - To get the results of all valid statistical tests, use `.hedges_g.statistical_tests` - - - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.hedges_g.results - - - - -.. raw:: html - -
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxpvalue_permutationpermutation_countbootstrapsresamplesrandom_seedpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020Hedges' gFalse1.025525950.3493941.618579(42, 4724)0.4728441.74166(125, 4875)0.0015000[-0.3617512915188043, -0.06120428036887727, 0....5000123450.002094-3.3088060.002057-3.3088060.00162583.0
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- - - -Producing estimation plots --------------------------- - -To produce a **Gardner-Altman estimation plot**, simply use the -``.plot()`` method. You can read more about its genesis and design -inspiration at :doc:`robust-beautiful`. - -Every effect size instance has access to the ``.plot()`` method. This -means you can quickly create plots for different effect sizes easily. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.mean_diff.plot(); - - - -.. image:: _images/tutorial_27_0.png - - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.hedges_g.plot(); - - - -.. image:: _images/tutorial_28_0.png - - -Instead of a Gardner-Altman plot, you can produce a **Cumming estimation -plot** by setting ``float_contrast=False`` in the ``plot()`` method. -This will plot the bootstrap effect sizes below the raw data, and also -displays the the mean (gap) and ± standard deviation of each group -(vertical ends) as gapped lines. This design was inspired by Edward -Tufte’s dictum to maximise the data-ink ratio. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.hedges_g.plot(float_contrast=False); - - - -.. image:: _images/tutorial_30_0.png - - -For paired data, we use -`slopegraphs `__ -(another innovation from Edward Tufte) to connect paired observations. -Both Gardner-Altman and Cumming plots support this. - -.. code-block:: python3 - :linenos: - - - two_groups_paired.mean_diff.plot(); - - - -.. image:: _images/tutorial_32_0.png - - -.. code-block:: python3 - :linenos: - - - two_groups_paired.mean_diff.plot(float_contrast=False); - - - -.. image:: _images/tutorial_33_0.png - - -The ``dabest`` package also implements a range of estimation plot -designs aimed at depicting common experimental designs. - -The **multi-two-group estimation plot** tiles two or more Cumming plots -horizontally, and is created by passing a *nested tuple* to ``idx`` when -``dabest.load()`` is first invoked. - -Thus, the lower axes in the Cumming plot is effectively a `forest -plot `__, used in -meta-analyses to aggregate and compare data from different experiments. - -.. code-block:: python3 - :linenos: - - - multi_2group = dabest.load(df, idx=(("Control 1", "Test 1",), - ("Control 2", "Test 2") - )) - - multi_2group.mean_diff.plot(); - - - -.. image:: _images/tutorial_35_0.png - - -The multi-two-group design also accomodates paired comparisons. - -.. code-block:: python3 - :linenos: - - - multi_2group_paired = dabest.load(df, idx=(("Control 1", "Test 1"), - ("Control 2", "Test 2") - ), - paired=True, id_col="ID" - ) - - multi_2group_paired.mean_diff.plot(); - - - -.. image:: _images/tutorial_37_0.png - - -The **shared control plot** displays another common experimental -paradigm, where several test samples are compared against a common -reference sample. - -This type of Cumming plot is automatically generated if the tuple passed -to ``idx`` has more than two data columns. - -.. code-block:: python3 - :linenos: - - - shared_control = dabest.load(df, idx=("Control 1", "Test 1", - "Test 2", "Test 3", - "Test 4", "Test 5", "Test 6") - ) - -.. code-block:: python3 - :linenos: - - - shared_control - - - - -.. parsed-literal:: - - DABEST v0.3.0 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:54 2020. - - Effect size(s) with 95% confidence intervals will be computed for: - 1. Test 1 minus Control 1 - 2. Test 2 minus Control 1 - 3. Test 3 minus Control 1 - 4. Test 4 minus Control 1 - 5. Test 5 minus Control 1 - 6. Test 6 minus Control 1 - - 5000 resamples will be used to generate the effect size bootstraps. - - - -.. code-block:: python3 - :linenos: - - - shared_control.mean_diff - - - - -.. parsed-literal:: - - DABEST v0.3.0 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:58 2020. - - The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768]. - The p-value of the two-sided permutation t-test is 0.001. - - The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.914, -0.211]. - The p-value of the two-sided permutation t-test is 0.0042. - - The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.295, 0.628]. - The p-value of the two-sided permutation t-test is 0.479. - - The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.306, 1.31]. - The p-value of the two-sided permutation t-test is 0.0042. - - The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0137, 0.497]. - The p-value of the two-sided permutation t-test is 0.0404. - - The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI -0.00441, 0.515]. - The p-value of the two-sided permutation t-test is 0.0324. - - 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated. - The p-value(s) reported are the likelihood(s) of observing the effect size(s), - if the null hypothesis of zero difference is true. - For each p-value, 5000 reshuffles of the control and test labels were performed. - - To get the results of all valid statistical tests, use `.mean_diff.statistical_tests` - - - -.. code-block:: python3 - :linenos: - - - shared_control.mean_diff.plot(); - - - -.. image:: _images/tutorial_42_0.png - - -``dabest`` thus empowers you to robustly perform and elegantly present -complex visualizations and statistics. - -.. code-block:: python3 - :linenos: - - - multi_groups = dabest.load(df, idx=(("Control 1", "Test 1",), - ("Control 2", "Test 2","Test 3"), - ("Control 3", "Test 4","Test 5", "Test 6") - )) - - -.. code-block:: python3 - :linenos: - - - multi_groups - - - - -.. parsed-literal:: - - DABEST v0.3.0 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:12:58 2020. - - Effect size(s) with 95% confidence intervals will be computed for: - 1. Test 1 minus Control 1 - 2. Test 2 minus Control 2 - 3. Test 3 minus Control 2 - 4. Test 4 minus Control 3 - 5. Test 5 minus Control 3 - 6. Test 6 minus Control 3 - - 5000 resamples will be used to generate the effect size bootstraps. - - - -.. code-block:: python3 - :linenos: - - - multi_groups.mean_diff - - - - -.. parsed-literal:: - - DABEST v0.3.0 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:13:02 2020. - - The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768]. - The p-value of the two-sided permutation t-test is 0.001. - - The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.895]. - The p-value of the two-sided permutation t-test is 0.0. - - The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.3, -0.103]. - The p-value of the two-sided permutation t-test is 0.0352. - - The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.114, 0.887]. - The p-value of the two-sided permutation t-test is 0.161. - - The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.404, 0.0742]. - The p-value of the two-sided permutation t-test is 0.208. - - The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.398, 0.102]. - The p-value of the two-sided permutation t-test is 0.282. - - 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated. - The p-value(s) reported are the likelihood(s) of observing the effect size(s), - if the null hypothesis of zero difference is true. - For each p-value, 5000 reshuffles of the control and test labels were performed. - - To get the results of all valid statistical tests, use `.mean_diff.statistical_tests` - - - -.. code-block:: python3 - :linenos: - - - multi_groups.mean_diff.plot(); - - - -.. image:: _images/tutorial_47_0.png - - -Using long (aka ‘melted’) data frames -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -``dabest`` can also work with ‘melted’ or ‘long’ data. This term is so -used because each row will now correspond to a single datapoint, with -one column carrying the value and other columns carrying ‘metadata’ -describing that datapoint. - -More details on wide vs long or ‘melted’ data can be found in this -`Wikipedia -article `__. The -`pandas -documentation `__ -gives recipes for melting dataframes. - -.. code-block:: python3 - :linenos: - - - x='group' - y='metric' - - value_cols = df.columns[:-2] # select all but the "Gender" and "ID" columns. - - df_melted = pd.melt(df.reset_index(), - id_vars=["Gender", "ID"], - value_vars=value_cols, - value_name=y, - var_name=x) - - df_melted.head() # Gives the first five rows of `df_melted`. - - - - -.. raw:: html - -
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GenderIDgroupmetric
0Female1Control 12.793984
1Female2Control 13.236759
2Female3Control 13.019149
3Female4Control 12.804638
4Female5Control 12.858019
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- - - -When your data is in this format, you will need to specify the ``x`` and -``y`` columns in ``dabest.load()``. - -.. code-block:: python3 - :linenos: - - - analysis_of_long_df = dabest.load(df_melted, idx=("Control 1", "Test 1"), - x="group", y="metric") - - analysis_of_long_df - - - - -.. parsed-literal:: - - DABEST v0.3.0 - ============= - - Good afternoon! - The current time is Mon Oct 19 17:13:03 2020. - - Effect size(s) with 95% confidence intervals will be computed for: - 1. Test 1 minus Control 1 - - 5000 resamples will be used to generate the effect size bootstraps. - - - -.. code-block:: python3 - :linenos: - - - analysis_of_long_df.mean_diff.plot(); - - - -.. image:: _images/tutorial_52_0.png - - -Controlling plot aesthetics -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Changing the y-axes labels. - -.. code-block:: python3 - :linenos: - - - two_groups_unpaired.mean_diff.plot(swarm_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!"); - - - -.. image:: _images/tutorial_55_0.png - - -Color the rawdata according to another column in the dataframe. - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(color_col="Gender"); - - - -.. image:: _images/tutorial_57_0.png - - -.. code-block:: python3 - :linenos: - - - two_groups_paired.mean_diff.plot(color_col="Gender"); - - - -.. image:: _images/tutorial_58_0.png - - -Changing the palette used with ``custom_palette``. Any valid matplotlib -or seaborn color palette is accepted. - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(color_col="Gender", custom_palette="Dark2"); - - - -.. image:: _images/tutorial_60_0.png - - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(custom_palette="Paired"); - - - -.. image:: _images/tutorial_61_0.png - - -You can also create your own color palette. Create a dictionary where -the keys are group names, and the values are valid matplotlib colors. - -You can specify matplotlib colors in a `variety of -ways `__. Here, I demonstrate -using named colors, hex strings (commonly used on the web), and RGB -tuples. - -.. code-block:: python3 - :linenos: - - - my_color_palette = {"Control 1" : "blue", - "Test 1" : "purple", - "Control 2" : "#cb4b16", # This is a hex string. - "Test 2" : (0., 0.7, 0.2) # This is a RGB tuple. - } - - multi_2group.mean_diff.plot(custom_palette=my_color_palette); - - - -.. image:: _images/tutorial_63_0.png - - -By default, ``dabest.plot()`` will -`desaturate `__ -the color of the dots in the swarmplot by 50%. This draws attention to -the effect size bootstrap curves. - -You can alter the default values with the ``swarm_desat`` and -``halfviolin_desat`` keywords. - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(custom_palette=my_color_palette, - swarm_desat=0.75, - halfviolin_desat=0.25); - - - -.. image:: _images/tutorial_65_0.png - - -You can also change the sizes of the dots used in the rawdata swarmplot, -and those used to indicate the effect sizes. - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(raw_marker_size=3, - es_marker_size=12); - - - -.. image:: _images/tutorial_67_0.png - - -Changing the y-limits for the rawdata axes, and for the contrast axes. - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(swarm_ylim=(0, 5), - contrast_ylim=(-2, 2)); - - - -.. image:: _images/tutorial_69_0.png - - -If your effect size is qualitatively inverted (ie. a smaller value is a -better outcome), you can simply invert the tuple passed to -``contrast_ylim``. - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - - - -.. image:: _images/tutorial_71_0.png - - -You can add minor ticks and also change the tick frequency by accessing -the axes directly. - -Each estimation plot produced by ``dabest`` has 2 axes. The first one -contains the rawdata swarmplot; the second one contains the bootstrap -effect size differences. - -.. code-block:: python3 - :linenos: - - - import matplotlib.ticker as Ticker - - f = two_groups_unpaired.mean_diff.plot() - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - - -.. image:: _images/tutorial_73_0.png - - -.. code-block:: python3 - :linenos: - - - f = multi_2group.mean_diff.plot(swarm_ylim=(0,6), - contrast_ylim=(-3, 1)) - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(2)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(1)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - - -.. image:: _images/tutorial_74_0.png - - -.. _inset plot: - -Creating estimation plots in existing axes -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -*Implemented in v0.2.6 by Adam Nekimken*. - -``dabest.plot`` has an ``ax`` keyword that accepts any Matplotlib -``Axes``. The entire estimation plot will be created in the specified -``Axes``. - -.. code-block:: python3 - :linenos: - - - from matplotlib import pyplot as plt - f, axx = plt.subplots(nrows=2, ncols=2, - figsize=(15, 15), - gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing. - ) - - two_groups_unpaired.mean_diff.plot(ax=axx.flat[0]); - - two_groups_paired.mean_diff.plot(ax=axx.flat[1]); - - multi_2group.mean_diff.plot(ax=axx.flat[2]); - - multi_2group_paired.mean_diff.plot(ax=axx.flat[3]); - - - -.. image:: _images/tutorial_76_0.png - - -In this case, to access the individual rawdata axes, use -``name_of_axes`` to manipulate the rawdata swarmplot axes, and -``name_of_axes.contrast_axes`` to gain access to the effect size axes. - -.. code-block:: python3 - :linenos: - - - topleft_axes = axx.flat[0] - topleft_axes.set_ylabel("New y-axis label for rawdata") - topleft_axes.contrast_axes.set_ylabel("New y-axis label for effect size") - - f - - - - -.. image:: _images/tutorial_78_0.png - - - -Applying style sheets -~~~~~~~~~~~~~~~~~~~~~ - -*Implemented in v0.2.0*. - -``dabest`` can apply `matplotlib style -sheets `__ -to estimation plots. You can refer to this -`gallery `__ -of style sheets for reference. - -.. code-block:: python3 - :linenos: - - - import matplotlib.pyplot as plt - plt.style.use("dark_background") - -.. code-block:: python3 - :linenos: - - - multi_2group.mean_diff.plot(); - - - -.. image:: _images/tutorial_81_0.png diff --git a/iris.png b/iris.png index fc3cce37..92d58275 100644 Binary files a/iris.png and b/iris.png differ diff --git a/nbs/01-getting_started.ipynb b/nbs/01-getting_started.ipynb new file mode 100644 index 00000000..680bbfc5 --- /dev/null +++ b/nbs/01-getting_started.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "68f51e09", + "metadata": {}, + "source": [ + "# Getting Started\n", + "\n", + "> Requirements and installation of dabest.\n", + "\n", + "- order: 1" + ] + }, + { + "cell_type": "markdown", + "id": "d1d5cb1a", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "markdown", + "id": "d2aca73b", + "metadata": {}, + "source": [ + "\n", + "\n", + "Python 3.8 is strongly recommended. DABEST has also been tested with Python 3.6 and 3.7.\n", + "\n", + "In addition, the following packages are also required (listed with their minimal versions):\n", + "\n", + "* [numpy 1.22.3](https://www.numpy.org)\n", + "* [scipy 1.9.3](https://www.scipy.org)\n", + "* [matplotlib 3.5.1](https://www.matplotlib.org)\n", + "* [pandas 1.5.0](https://pandas.pydata.org)\n", + "* [seaborn 0.11.2](https://seaborn.pydata.org)\n", + "* [lqrt 0.3](https://github.com/alyakin314/lqrt)\n", + "\n", + "To obtain these package dependencies easily, it is highly recommended to download the [Anaconda](https://www.continuum.io/downloads) distribution of Python.\n" + ] + }, + { + "cell_type": "markdown", + "id": "c0de00b2", + "metadata": {}, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "markdown", + "id": "304a3639", + "metadata": {}, + "source": [ + "1. Using ``pip``\n", + "\n", + "At the command line, run\n", + "\n", + "\n", + "**$ pip install dabest**\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "89d3cfb0", + "metadata": {}, + "source": [ + "2. Using Github\n", + "\n", + "Clone the [DABEST-python repo](https://github.com/ACCLAB/DABEST-python) locally (see instructions [here] (https://help.github.com/articles/cloning-a-repository/).\n", + "\n", + "Then, navigate to the cloned repo in the command line and run\n", + "\n", + "**$ pip install**" + ] + }, + { + "cell_type": "markdown", + "id": "2e7dc4c0", + "metadata": {}, + "source": [ + "## Testing" + ] + }, + { + "cell_type": "markdown", + "id": "a9f8cb3e", + "metadata": {}, + "source": [ + "To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest/).\n", + "\n", + "Run ``pytest`` in the root directory of the source distribution. This runs the test suite in ``dabest/tests`` folder. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "31d110fe", + "metadata": {}, + "source": [ + "## Bugs" + ] + }, + { + "cell_type": "markdown", + "id": "c4d6be79", + "metadata": {}, + "source": [ + "Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/DABEST-python/issues/new) for DABEST-python.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ced1616b", + "metadata": {}, + "source": [ + "## Contributing" + ] + }, + { + "cell_type": "markdown", + "id": "99c17867", + "metadata": {}, + "source": [ + "All contributions are welcome. Please fork the [Github repo](https://github.com/ACCLAB/DABEST-python/) and open a pull request.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23a7b823", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/02-about.ipynb b/nbs/02-about.ipynb new file mode 100644 index 00000000..f8ab1ad6 --- /dev/null +++ b/nbs/02-about.ipynb @@ -0,0 +1,99 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c67c8b91", + "metadata": {}, + "source": [ + "# About" + ] + }, + { + "cell_type": "markdown", + "id": "2dbd3752", + "metadata": {}, + "source": [ + "## Authors\n", + "\n", + "DABEST is written in Python by [Joses W. Ho](https://twitter.com/jacuzzijo), with design and input from [Adam Claridge-Chang](https://twitter.com/adamcchang) and other [lab members](https://www.claridgechang.net/people.html).\n", + "\n", + "Additional features in v2023.02.14 were added by [Yixuan Li](https://github.com/LI-Yixuan), [Zinan Lu](https://github.com/Jacobluke-) and [Rou Zhang](https://github.com/ZHANGROU-99).\n", + "\n", + "To find out more about the authors' research, please visit the [Claridge-Chang lab webpage](http://www.claridgechang.net/).\n", + "\n", + "## Contributors\n", + "\n", + "\n", + "- Statistics supervision by Hyungwon Choi\n", + "\n", + "- Alpha testers from the Claridge-Chang lab: [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, Stanislav Ott, Tayfun Tumkaya, Jonathan Anns, Nicole Lee and Yishan Mai.\n", + "\n", + "- DizietAsahi ([DizietAsahi](https://github.com/DizietAsahi)) with [PR #86](https://github.com/ACCLAB/DABEST-python/pull/86): Fix bugs in slopegraph and reference line keyword parsing.\n", + "\n", + "- Adam Li ([@adam2392](https://github.com/adam2392)) with [PR #85](https://github.com/ACCLAB/DABEST-python/pull/85): Implement [Lq-Likelihood-Ratio-Type Test](https://github.com/alyakin314/lqrt) in statistical output.\n", + "\n", + "- Mason Malone ([@MasonM](https://github.com/MasonM)) with [PR #30](https://github.com/ACCLAB/DABEST-python/pull/30): Fix plot error when effect size is 0.\n", + "\n", + "- Matthew Edwards ([@mje-nz](https://github.com/mje-nz)) with [PR #71](https://github.com/ACCLAB/DABEST-python/pull/30): Specify dependencies correctly in ``setup.py``. \n", + "\n", + "- Adam Nekimken ([@anekimken](https://github.com/anekimken)) with [PR #73](https://github.com/ACCLAB/DABEST-python/pull/73): Implement inset axes so estimation plots can be plotted on a pre-determined :py:mod:`matplotlib` :py:class:`Axes` object.\n", + "\n", + "\n", + "\n", + "## Typography\n", + "\n", + "\n", + "This documentation uses [Spectral](https://spectral.prototypo.io/) for the body text, [Merriweather Sans](https://ebensorkin.wordpress.com/) for the side bar and titles, and [IBM Plex Mono](https://github.com/IBM/plex) for the monospace code font.\n", + "\n", + "\n", + "## License\n", + "\n", + "\n", + "The DABEST package in Python is licenced under the [BSD 3-clause Clear License](https://choosealicense.com/licenses/bsd-3-clause-clear/).\n", + "\n", + "Copyright (c) 2016-2023, Joses W. Ho\n", + "All rights reserved.\n", + "\n", + "Redistribution and use in source and binary forms, with or without\n", + "modification, are permitted (subject to the limitations in the disclaimer\n", + "below) provided that the following conditions are met:\n", + "\n", + " * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n", + "\n", + " * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n", + "\n", + " * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n", + "\n", + "NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY\n", + "THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\n", + "CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n", + "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n", + "PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n", + "CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n", + "EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n", + "PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\n", + "BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\n", + "IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n", + "ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n", + "POSSIBILITY OF SUCH DAMAGE.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de35a697", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/03-citation.ipynb b/nbs/03-citation.ipynb new file mode 100644 index 00000000..27cdb437 --- /dev/null +++ b/nbs/03-citation.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "95d72bf3", + "metadata": {}, + "source": [ + "# Citing DABEST" + ] + }, + { + "cell_type": "markdown", + "id": "b0833edd", + "metadata": {}, + "source": [ + "\n", + "\n", + "If your publication features a graphic generated with this software library, please cite the following publication.\n", + "\n", + "**Moving beyond P values: Everyday data analysis with estimation plots**\n", + "Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang\n", + "\n", + "`Nature Methods` 2019, 1548-7105. [doi:10.1038/s41592-019-0470-3](https://doi.org/10.1038/s41592-019-0470-3)\n", + "\n", + "[Free-to-view PDF](https://rdcu.be/bHhJ4)\n", + "\n", + "[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "808f8708", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/bootstrap.ipynb b/nbs/API/bootstrap.ipynb new file mode 100644 index 00000000..2a1a0970 --- /dev/null +++ b/nbs/API/bootstrap.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b391aa10", + "metadata": {}, + "source": [ + "# Bootstrap\n", + "\n", + "\n", + "- order: 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c45d5de", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _bootstrap_tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc72d776", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()\n", + "from __future__ import division" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4231300f", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e86b4b8d", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "class bootstrap:\n", + " '''\n", + " Computes the summary statistic and a bootstrapped confidence interval. \n", + " \n", + " Returns\n", + " -------\n", + " An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used:\n", + " `summary`: float.\n", + " The summary statistic.\n", + " `is_difference`: boolean.\n", + " Whether or not the summary is the difference between two groups. If False, only x1 was supplied.\n", + " `is_paired`: string, default None\n", + " The type of the experiment under which the data are obtained\n", + " `statistic`: callable\n", + " The function used to compute the summary.\n", + " `reps`: int\n", + " The number of bootstrap iterations performed.\n", + " `stat_array`:array\n", + " A sorted array of values obtained by bootstrapping the input arrays.\n", + " `ci`:float\n", + " The size of the confidence interval reported (in percentage).\n", + " `pct_ci_low,pct_ci_high`:floats\n", + " The upper and lower bounds of the confidence interval as computed by taking the percentage bounds.\n", + " `pct_low_high_indices`:array\n", + " An array with the indices in `stat_array` corresponding to the percentage confidence interval bounds.\n", + " `bca_ci_low, bca_ci_high`: floats\n", + " The upper and lower bounds of the bias-corrected and accelerated(BCa) confidence interval. See Efron 1977.\n", + " `bca_low_high_indices`: array\n", + " An array with the indices in `stat_array` corresponding to the BCa confidence interval bounds.\n", + " `pvalue_1samp_ttest`: float\n", + " P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were passed (x1 and x2), returns 'NIL'. See \n", + " `pvalue_2samp_ind_ttest`: float\n", + " P-value obtained from scipy.stats.ttest_ind. If a single array was given (x1 only), or if `paired` is not None, returns 'NIL'. See \n", + " `pvalue_2samp_related_ttest`: float\n", + " P-value obtained from scipy.stats.ttest_rel. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. See \n", + " `pvalue_wilcoxon`: float\n", + " P-value obtained from scipy.stats.wilcoxon. If a single array was given (x1 only), or if `paired` is None, returns 'NIL'. The Wilcoxons signed-rank test is a nonparametric paired test of the null hypothesis that the related samples x1 and x2 are from the same distribution. See \n", + " `pvalue_mann_whitney`: float\n", + " Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns 'NIL'. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See \n", + "\n", + " '''\n", + " def __init__(self, \n", + " x1:np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", + " x2:np.array=None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", + " paired:bool=False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control).\n", + " statfunction:callable=np.mean,#The summary statistic called on data.\n", + " smoothboot:bool=False,#Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).\n", + " alpha_level:float=0.05,#Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n", + " reps:int=5000 # Number of bootstrap iterations to perform.\n", + " ):\n", + "\n", + " import numpy as np\n", + " import pandas as pd\n", + " import seaborn as sns\n", + "\n", + " from scipy.stats import norm\n", + " from numpy.random import randint\n", + " from scipy.stats import ttest_1samp, ttest_ind, ttest_rel\n", + " from scipy.stats import mannwhitneyu, wilcoxon, norm\n", + " import warnings\n", + "\n", + " # Turn to pandas series.\n", + " x1 = pd.Series(x1).dropna()\n", + " diff = False\n", + "\n", + " # Initialise statfunction\n", + " if statfunction == None:\n", + " statfunction = np.mean\n", + "\n", + " # Compute two-sided alphas.\n", + " if alpha_level > 1. or alpha_level < 0.:\n", + " raise ValueError(\"alpha_level must be between 0 and 1.\")\n", + " alphas = np.array([alpha_level/2., 1-alpha_level/2.])\n", + "\n", + " sns_bootstrap_kwargs = {'func': statfunction,\n", + " 'n_boot': reps,\n", + " 'smooth': smoothboot}\n", + "\n", + " if paired:\n", + " # check x2 is not None:\n", + " if x2 is None:\n", + " raise ValueError('Please specify x2.')\n", + " else:\n", + " x2 = pd.Series(x2).dropna()\n", + " if len(x1) != len(x2):\n", + " raise ValueError('x1 and x2 are not the same length.')\n", + "\n", + " if (x2 is None) or (paired is not None) :\n", + "\n", + " if x2 is None:\n", + " tx = x1\n", + " paired = False\n", + " ttest_single = ttest_1samp(x1, 0)[1]\n", + " ttest_2_ind = 'NIL'\n", + " ttest_2_paired = 'NIL'\n", + " wilcoxonresult = 'NIL'\n", + "\n", + " elif paired is not None:\n", + " diff = True\n", + " tx = x2 - x1\n", + " ttest_single = 'NIL'\n", + " ttest_2_ind = 'NIL'\n", + " ttest_2_paired = ttest_rel(x1, x2)[1]\n", + " wilcoxonresult = wilcoxon(x1, x2)[1]\n", + " mannwhitneyresult = 'NIL'\n", + "\n", + " # Turns data into array, then tuple.\n", + " tdata = (tx,)\n", + "\n", + " # The value of the statistic function applied\n", + " # just to the actual data.\n", + " summ_stat = statfunction(*tdata)\n", + " statarray = sns.algorithms.bootstrap(tx, **sns_bootstrap_kwargs)\n", + " statarray.sort()\n", + "\n", + " # Get Percentile indices\n", + " pct_low_high = np.round((reps-1) * alphas)\n", + " pct_low_high = np.nan_to_num(pct_low_high).astype('int')\n", + "\n", + "\n", + " elif x2 is not None and paired is None:\n", + " diff = True\n", + " x2 = pd.Series(x2).dropna()\n", + " # Generate statarrays for both arrays.\n", + " ref_statarray = sns.algorithms.bootstrap(x1, **sns_bootstrap_kwargs)\n", + " exp_statarray = sns.algorithms.bootstrap(x2, **sns_bootstrap_kwargs)\n", + "\n", + " tdata = exp_statarray - ref_statarray\n", + " statarray = tdata.copy()\n", + " statarray.sort()\n", + " tdata = (tdata, ) # Note tuple form.\n", + "\n", + " # The difference as one would calculate it.\n", + " summ_stat = statfunction(x2) - statfunction(x1)\n", + "\n", + " # Get Percentile indices\n", + " pct_low_high = np.round((reps-1) * alphas)\n", + " pct_low_high = np.nan_to_num(pct_low_high).astype('int')\n", + "\n", + " # Statistical tests.\n", + " ttest_single='NIL'\n", + " ttest_2_ind = ttest_ind(x1,x2)[1]\n", + " ttest_2_paired='NIL'\n", + " mannwhitneyresult = mannwhitneyu(x1, x2, alternative='two-sided')[1]\n", + " wilcoxonresult = 'NIL'\n", + "\n", + " # Get Bias-Corrected Accelerated indices convenience function invoked.\n", + " bca_low_high = bca(tdata, alphas, statarray,\n", + " statfunction, summ_stat, reps)\n", + "\n", + " # Warnings for unstable or extreme indices.\n", + " for ind in [pct_low_high, bca_low_high]:\n", + " if np.any(ind == 0) or np.any(ind == reps-1):\n", + " warnings.warn(\"Some values used extremal samples;\"\n", + " \" results are probably unstable.\")\n", + " elif np.any(ind<10) or np.any(ind>=reps-10):\n", + " warnings.warn(\"Some values used top 10 low/high samples;\"\n", + " \" results may be unstable.\")\n", + "\n", + " self.summary = summ_stat\n", + " self.is_paired = paired\n", + " self.is_difference = diff\n", + " self.statistic = str(statfunction)\n", + " self.n_reps = reps\n", + "\n", + " self.ci = (1-alpha_level)*100\n", + " self.stat_array = np.array(statarray)\n", + "\n", + " self.pct_ci_low = statarray[pct_low_high[0]]\n", + " self.pct_ci_high = statarray[pct_low_high[1]]\n", + " self.pct_low_high_indices = pct_low_high\n", + "\n", + " self.bca_ci_low = statarray[bca_low_high[0]]\n", + " self.bca_ci_high = statarray[bca_low_high[1]]\n", + " self.bca_low_high_indices = bca_low_high\n", + "\n", + " self.pvalue_1samp_ttest = ttest_single\n", + " self.pvalue_2samp_ind_ttest = ttest_2_ind\n", + " self.pvalue_2samp_paired_ttest = ttest_2_paired\n", + " self.pvalue_wilcoxon = wilcoxonresult\n", + " self.pvalue_mann_whitney = mannwhitneyresult\n", + "\n", + " self.results = {'stat_summary': self.summary,\n", + " 'is_difference': diff,\n", + " 'is_paired': paired,\n", + " 'bca_ci_low': self.bca_ci_low,\n", + " 'bca_ci_high': self.bca_ci_high,\n", + " 'ci': self.ci\n", + " }\n", + "\n", + " def __repr__(self):\n", + " import numpy as np\n", + "\n", + " if 'mean' in self.statistic:\n", + " stat = 'mean'\n", + " elif 'median' in self.statistic:\n", + " stat = 'median'\n", + " else:\n", + " stat = self.statistic\n", + "\n", + " diff_types = {'sequential': 'paired', 'baseline': 'paired', None: 'unpaired'}\n", + " if self.is_difference:\n", + " a = 'The {} {} difference is {}.'.format(diff_types[self.is_paired],\n", + " stat, self.summary)\n", + " else:\n", + " a = 'The {} is {}.'.format(stat, self.summary)\n", + "\n", + " b = '[{} CI: {}, {}]'.format(self.ci, self.bca_ci_low, self.bca_ci_high)\n", + " return '\\n'.join([a, b])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00c814b9", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def jackknife_indexes(data):\n", + " # Taken without modification from scikits.bootstrap package.\n", + " \"\"\"\n", + " From the scikits.bootstrap package.\n", + " Given an array, returns a list of arrays where each array is a set of\n", + " jackknife indexes.\n", + "\n", + " For a given set of data Y, the jackknife sample J[i] is defined as the\n", + " data set Y with the ith data point deleted.\n", + " \"\"\"\n", + " import numpy as np\n", + "\n", + " base = np.arange(0,len(data))\n", + " return (np.delete(base,i) for i in base)\n", + "\n", + "def bca(data, alphas, statarray, statfunction, ostat, reps):\n", + " '''\n", + " Subroutine called to calculate the BCa statistics.\n", + " Borrowed heavily from scikits.bootstrap code.\n", + " '''\n", + " import warnings\n", + "\n", + " import numpy as np\n", + " import pandas as pd\n", + " import seaborn as sns\n", + "\n", + " from scipy.stats import norm\n", + " from numpy.random import randint\n", + "\n", + " # The bias correction value.\n", + " z0 = norm.ppf( ( 1.0*np.sum(statarray < ostat, axis = 0) ) / reps )\n", + "\n", + " # Statistics of the jackknife distribution\n", + " jackindexes = jackknife_indexes(data[0])\n", + " jstat = [statfunction(*(x[indexes] for x in data))\n", + " for indexes in jackindexes]\n", + " jmean = np.mean(jstat,axis = 0)\n", + "\n", + " # Acceleration value\n", + " a = np.divide(np.sum( (jmean - jstat)**3, axis = 0 ),\n", + " ( 6.0 * np.sum( (jmean - jstat)**2, axis = 0)**1.5 )\n", + " )\n", + " if np.any(np.isnan(a)):\n", + " nanind = np.nonzero(np.isnan(a))\n", + " warnings.warn(\"Some acceleration values were undefined.\"\n", + " \"This is almost certainly because all values\"\n", + " \"for the statistic were equal. Affected\"\n", + " \"confidence intervals will have zero width and\"\n", + " \"may be inaccurate (indexes: {})\".format(nanind))\n", + " zs = z0 + norm.ppf(alphas).reshape(alphas.shape+(1,)*z0.ndim)\n", + " avals = norm.cdf(z0 + zs/(1-a*zs))\n", + " nvals = np.round((reps-1)*avals)\n", + " nvals = np.nan_to_num(nvals).astype('int')\n", + "\n", + " return nvals" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/class.ipynb b/nbs/API/class.ipynb new file mode 100644 index 00000000..b0ce23a3 --- /dev/null +++ b/nbs/API/class.ipynb @@ -0,0 +1,4103 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ed122c74", + "metadata": {}, + "source": [ + "# Class\n", + "\n", + "> Several classes for estimating statistics and generating plots.\n", + "\n", + "- order: 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb97d9b1", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d5d586f", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcd32470", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3c6f47a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import numpy as np\n", + "from scipy.stats import norm\n", + "import pandas as pd\n", + "from scipy.stats import randint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "204a64b4", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "350b12c1", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class Dabest(object):\n", + "\n", + " \"\"\"\n", + " Class for estimation statistics and plots.\n", + " \"\"\"\n", + "\n", + " def __init__(self, data, idx, x, y, paired, id_col, ci, \n", + " resamples, random_seed, proportional, delta2, \n", + " experiment, experiment_label, x1_level, mini_meta):\n", + "\n", + " \"\"\"\n", + " Parses and stores pandas DataFrames in preparation for estimation\n", + " statistics. You should not be calling this class directly; instead,\n", + " use `dabest.load()` to parse your DataFrame prior to analysis.\n", + " \"\"\"\n", + "\n", + " # Import standard data science libraries.\n", + " import numpy as np\n", + " import pandas as pd\n", + " import seaborn as sns\n", + "\n", + " self.__delta2 = delta2\n", + " self.__experiment = experiment\n", + " self.__ci = ci\n", + " self.__data = data\n", + " self.__id_col = id_col\n", + " self.__is_paired = paired\n", + " self.__resamples = resamples\n", + " self.__random_seed = random_seed\n", + " self.__proportional = proportional\n", + " self.__mini_meta = mini_meta \n", + "\n", + " # Make a copy of the data, so we don't make alterations to it.\n", + " data_in = data.copy()\n", + " # data_in.reset_index(inplace=True)\n", + " # data_in_index_name = data_in.index.name\n", + "\n", + "\n", + " # Check if it is a valid mini_meta case\n", + " if mini_meta is True:\n", + "\n", + " # Only mini_meta calculation but not proportional and delta-delta function\n", + " if proportional is True:\n", + " err0 = '`proportional` and `mini_meta` cannot be True at the same time.'\n", + " raise ValueError(err0)\n", + " elif delta2 is True:\n", + " err0 = '`delta` and `mini_meta` cannot be True at the same time.'\n", + " raise ValueError(err0)\n", + " \n", + " # Check if the columns stated are valid\n", + " if all([isinstance(i, str) for i in idx]):\n", + " if len(pd.unique([t for t in idx]).tolist())!=2:\n", + " err0 = '`mini_meta` is True, but `idx` ({})'.format(idx) \n", + " err1 = 'does not contain exactly 2 columns.'\n", + " raise ValueError(err0 + err1)\n", + " elif all([isinstance(i, (tuple, list)) for i in idx]):\n", + " all_idx_lengths = [len(t) for t in idx]\n", + " if (np.array(all_idx_lengths) != 2).any():\n", + " err1 = \"`mini_meta` is True, but some idx \"\n", + " err2 = \"in {} does not consist only of two groups.\".format(idx)\n", + " raise ValueError(err1 + err2)\n", + " \n", + "\n", + "\n", + " # Check if this is a 2x2 ANOVA case and x & y are valid columns\n", + " # Create experiment_label and x1_level\n", + " if delta2 is True:\n", + " if proportional is True:\n", + " err0 = '`proportional` and `delta` cannot be True at the same time.'\n", + " raise ValueError(err0)\n", + " # idx should not be specified\n", + " if idx:\n", + " err0 = '`idx` should not be specified when `delta2` is True.'.format(len(x))\n", + " raise ValueError(err0)\n", + "\n", + " # Check if x is valid\n", + " if len(x) != 2:\n", + " err0 = '`delta2` is True but the number of variables indicated by `x` is {}.'.format(len(x))\n", + " raise ValueError(err0)\n", + " else:\n", + " for i in x:\n", + " if i not in data_in.columns:\n", + " err = '{0} is not a column in `data`. Please check.'.format(i)\n", + " raise IndexError(err)\n", + "\n", + " # Check if y is valid\n", + " if not y:\n", + " err0 = '`delta2` is True but `y` is not indicated.'\n", + " raise ValueError(err0)\n", + " elif y not in data_in.columns:\n", + " err = '{0} is not a column in `data`. Please check.'.format(y)\n", + " raise IndexError(err)\n", + "\n", + " # Check if experiment is valid\n", + " if experiment not in data_in.columns:\n", + " err = '{0} is not a column in `data`. Please check.'.format(experiment)\n", + " raise IndexError(err)\n", + "\n", + " # Check if experiment_label is valid and create experiment when needed\n", + " if experiment_label:\n", + " if len(experiment_label) != 2:\n", + " err0 = '`experiment_label` does not have a length of 2.'\n", + " raise ValueError(err0)\n", + " else: \n", + " for i in experiment_label:\n", + " if i not in data_in[experiment].unique():\n", + " err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment)\n", + " raise IndexError(err)\n", + " else:\n", + " experiment_label = data_in[experiment].unique()\n", + "\n", + " # Check if x1_level is valid\n", + " if x1_level:\n", + " if len(x1_level) != 2:\n", + " err0 = '`x1_level` does not have a length of 2.'\n", + " raise ValueError(err0)\n", + " else: \n", + " for i in x1_level:\n", + " if i not in data_in[x[0]].unique():\n", + " err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment)\n", + " raise IndexError(err)\n", + "\n", + " else:\n", + " x1_level = data_in[x[0]].unique() \n", + " elif experiment is not None:\n", + " experiment_label = data_in[experiment].unique()\n", + " x1_level = data_in[x[0]].unique() \n", + " self.__experiment_label = experiment_label\n", + " self.__x1_level = x1_level\n", + "\n", + "\n", + " # # Check if idx is specified\n", + " # if delta2 is False and not idx:\n", + " # err = '`idx` is not a column in `data`. Please check.'\n", + " # raise IndexError(err)\n", + "\n", + "\n", + " # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case\n", + " if idx is None and x is not None and y is not None:\n", + " # add a new column which is a combination of experiment and the first variable\n", + " new_col_name = experiment+x[0]\n", + " while new_col_name in data_in.columns:\n", + " new_col_name += \"_\"\n", + " data_in[new_col_name] = data_in[x[0]].astype(str) + \" \" + data_in[experiment].astype(str)\n", + "\n", + " #create idx and record the first and second x variable \n", + " idx = []\n", + " for i in list(map(lambda x: str(x), experiment_label)):\n", + " temp = []\n", + " for j in list(map(lambda x: str(x), x1_level)):\n", + " temp.append(j + \" \" + i)\n", + " idx.append(temp)\n", + " \n", + " self.__idx = idx\n", + " self.__x1 = x[0]\n", + " self.__x2 = x[1]\n", + " x = new_col_name\n", + " else:\n", + " self.__idx = idx\n", + " self.__x1 = None\n", + " self.__x2 = None\n", + "\n", + "\n", + "\n", + " # Determine the kind of estimation plot we need to produce.\n", + " if all([isinstance(i, str) for i in idx]):\n", + " # flatten out idx.\n", + " all_plot_groups = pd.unique([t for t in idx]).tolist()\n", + " if len(idx) > len(all_plot_groups):\n", + " err0 = '`idx` contains duplicated groups. Please remove any duplicates and try again.'\n", + " raise ValueError(err0)\n", + " \n", + " # We need to re-wrap this idx inside another tuple so as to\n", + " # easily loop thru each pairwise group later on.\n", + " self.__idx = (idx,)\n", + "\n", + " elif all([isinstance(i, (tuple, list)) for i in idx]):\n", + " all_plot_groups = pd.unique([tt for t in idx for tt in t]).tolist()\n", + " \n", + " actual_groups_given = sum([len(i) for i in idx])\n", + " \n", + " if actual_groups_given > len(all_plot_groups):\n", + " err0 = 'Groups are repeated across tuples,'\n", + " err1 = ' or a tuple has repeated groups in it.'\n", + " err2 = ' Please remove any duplicates and try again.'\n", + " raise ValueError(err0 + err1 + err2)\n", + "\n", + " else: # mix of string and tuple?\n", + " err = 'There seems to be a problem with the idx you '\\\n", + " 'entered--{}.'.format(idx)\n", + " raise ValueError(err)\n", + "\n", + " # Having parsed the idx, check if it is a kosher paired plot,\n", + " # if so stated.\n", + " #if paired is True:\n", + " # all_idx_lengths = [len(t) for t in self.__idx]\n", + " # if (np.array(all_idx_lengths) != 2).any():\n", + " # err1 = \"`is_paired` is True, but some idx \"\n", + " # err2 = \"in {} does not consist only of two groups.\".format(idx)\n", + " # raise ValueError(err1 + err2)\n", + "\n", + " # Check if there is a typo on paired\n", + " if paired is not None:\n", + " if paired not in (\"baseline\", \"sequential\"):\n", + " err = '{} assigned for `paired` is not valid.'.format(paired)\n", + " raise ValueError(err)\n", + "\n", + "\n", + " # Determine the type of data: wide or long.\n", + " if x is None and y is not None:\n", + " err = 'You have only specified `y`. Please also specify `x`.'\n", + " raise ValueError(err)\n", + "\n", + " elif y is None and x is not None:\n", + " err = 'You have only specified `x`. Please also specify `y`.'\n", + " raise ValueError(err)\n", + "\n", + " # Identify the type of data that was passed in.\n", + " elif x is not None and y is not None:\n", + " # Assume we have a long dataset.\n", + " # check both x and y are column names in data.\n", + " if x not in data_in.columns:\n", + " err = '{0} is not a column in `data`. Please check.'.format(x)\n", + " raise IndexError(err)\n", + " if y not in data_in.columns:\n", + " err = '{0} is not a column in `data`. Please check.'.format(y)\n", + " raise IndexError(err)\n", + "\n", + " # check y is numeric.\n", + " if not np.issubdtype(data_in[y].dtype, np.number):\n", + " err = '{0} is a column in `data`, but it is not numeric.'.format(y)\n", + " raise ValueError(err)\n", + "\n", + " # check all the idx can be found in data_in[x]\n", + " for g in all_plot_groups:\n", + " if g not in data_in[x].unique():\n", + " err0 = '\"{0}\" is not a group in the column `{1}`.'.format(g, x)\n", + " err1 = \" Please check `idx` and try again.\"\n", + " raise IndexError(err0 + err1)\n", + "\n", + " # Select only rows where the value in the `x` column \n", + " # is found in `idx`.\n", + " plot_data = data_in[data_in.loc[:, x].isin(all_plot_groups)].copy()\n", + " \n", + " # plot_data.drop(\"index\", inplace=True, axis=1)\n", + "\n", + " # Assign attributes\n", + " self.__x = x\n", + " self.__y = y\n", + " self.__xvar = x\n", + " self.__yvar = y\n", + "\n", + " elif x is None and y is None:\n", + " # Assume we have a wide dataset.\n", + " # Assign attributes appropriately.\n", + " self.__x = None\n", + " self.__y = None\n", + " self.__xvar = \"group\"\n", + " self.__yvar = \"value\"\n", + "\n", + " # First, check we have all columns in the dataset.\n", + " for g in all_plot_groups:\n", + " if g not in data_in.columns:\n", + " err0 = '\"{0}\" is not a column in `data`.'.format(g)\n", + " err1 = \" Please check `idx` and try again.\"\n", + " raise IndexError(err0 + err1)\n", + " \n", + " set_all_columns = set(data_in.columns.tolist())\n", + " set_all_plot_groups = set(all_plot_groups)\n", + " id_vars = set_all_columns.difference(set_all_plot_groups)\n", + "\n", + " plot_data = pd.melt(data_in,\n", + " id_vars=id_vars,\n", + " value_vars=all_plot_groups,\n", + " value_name=self.__yvar,\n", + " var_name=self.__xvar)\n", + " \n", + " # Added in v0.2.7.\n", + " # remove any NA rows.\n", + " plot_data.dropna(axis=0, how='any', subset=[self.__yvar], inplace=True)\n", + "\n", + " \n", + " # Lines 131 to 140 added in v0.2.3.\n", + " # Fixes a bug that jammed up when the xvar column was already \n", + " # a pandas Categorical. Now we check for this and act appropriately.\n", + " if isinstance(plot_data[self.__xvar].dtype, \n", + " pd.CategoricalDtype) is True:\n", + " plot_data[self.__xvar].cat.remove_unused_categories(inplace=True)\n", + " plot_data[self.__xvar].cat.reorder_categories(all_plot_groups, \n", + " ordered=True, \n", + " inplace=True)\n", + " else:\n", + " plot_data.loc[:, self.__xvar] = pd.Categorical(plot_data[self.__xvar],\n", + " categories=all_plot_groups,\n", + " ordered=True)\n", + " \n", + " # # The line below was added in v0.2.4, removed in v0.2.5.\n", + " # plot_data.dropna(inplace=True)\n", + " \n", + " self.__plot_data = plot_data\n", + " \n", + " self.__all_plot_groups = all_plot_groups\n", + "\n", + "\n", + " # Sanity check that all idxs are paired, if so desired.\n", + " #if paired is True:\n", + " # if id_col is None:\n", + " # err = \"`id_col` must be specified if `is_paired` is set to True.\"\n", + " # raise IndexError(err)\n", + " # elif id_col not in plot_data.columns:\n", + " # err = \"{} is not a column in `data`. \".format(id_col)\n", + " # raise IndexError(err)\n", + "\n", + " # Check if `id_col` is valid\n", + " if paired:\n", + " if id_col is None:\n", + " err = \"`id_col` must be specified if `paired` is assigned with a not NoneType value.\"\n", + " raise IndexError(err)\n", + " elif id_col not in plot_data.columns:\n", + " err = \"{} is not a column in `data`. \".format(id_col)\n", + " raise IndexError(err)\n", + "\n", + " EffectSizeDataFrame_kwargs = dict(ci=ci, is_paired=paired,\n", + " random_seed=random_seed,\n", + " resamples=resamples,\n", + " proportional=proportional, \n", + " delta2=delta2, \n", + " experiment_label=self.__experiment_label,\n", + " x1_level=self.__x1_level,\n", + " x2=self.__x2,\n", + " mini_meta = mini_meta)\n", + "\n", + " self.__mean_diff = EffectSizeDataFrame(self, \"mean_diff\",\n", + " **EffectSizeDataFrame_kwargs)\n", + "\n", + " self.__median_diff = EffectSizeDataFrame(self, \"median_diff\",\n", + " **EffectSizeDataFrame_kwargs)\n", + "\n", + " self.__cohens_d = EffectSizeDataFrame(self, \"cohens_d\",\n", + " **EffectSizeDataFrame_kwargs)\n", + "\n", + " self.__cohens_h = EffectSizeDataFrame(self, \"cohens_h\",\n", + " **EffectSizeDataFrame_kwargs) \n", + "\n", + " self.__hedges_g = EffectSizeDataFrame(self, \"hedges_g\",\n", + " **EffectSizeDataFrame_kwargs)\n", + "\n", + " if not paired:\n", + " self.__cliffs_delta = EffectSizeDataFrame(self, \"cliffs_delta\",\n", + " **EffectSizeDataFrame_kwargs)\n", + " else:\n", + " self.__cliffs_delta = \"The data is paired; Cliff's delta is therefore undefined.\"\n", + "\n", + "\n", + " def __repr__(self):\n", + " from .__init__ import __version__\n", + " import datetime as dt\n", + " import numpy as np\n", + "\n", + " from .misc_tools import print_greeting\n", + "\n", + " # Removed due to the deprecation of is_paired\n", + " #if self.__is_paired:\n", + " # es = \"Paired e\"\n", + " #else:\n", + " # es = \"E\"\n", + "\n", + " greeting_header = print_greeting()\n", + "\n", + " RM_STATUS = {'baseline' : 'for repeated measures against baseline \\n', \n", + " 'sequential': 'for the sequential design of repeated-measures experiment \\n',\n", + " 'None' : ''\n", + " }\n", + "\n", + " PAIRED_STATUS = {'baseline' : 'Paired e', \n", + " 'sequential' : 'Paired e',\n", + " 'None' : 'E'\n", + " }\n", + "\n", + " first_line = {\"rm_status\" : RM_STATUS[str(self.__is_paired)],\n", + " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)]}\n", + "\n", + " s1 = \"{paired_status}ffect size(s) {rm_status}\".format(**first_line)\n", + " s2 = \"with {}% confidence intervals will be computed for:\".format(self.__ci)\n", + " desc_line = s1 + s2\n", + "\n", + " out = [greeting_header + \"\\n\\n\" + desc_line]\n", + "\n", + " comparisons = []\n", + "\n", + " if self.__is_paired == 'sequential':\n", + " for j, current_tuple in enumerate(self.__idx):\n", + " for ix, test_name in enumerate(current_tuple[1:]):\n", + " control_name = current_tuple[ix]\n", + " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", + " else:\n", + " for j, current_tuple in enumerate(self.__idx):\n", + " control_name = current_tuple[0]\n", + "\n", + " for ix, test_name in enumerate(current_tuple[1:]):\n", + " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", + "\n", + " if self.__delta2 is True:\n", + " comparisons.append(\"{} minus {} (only for mean difference)\".format(self.__experiment_label[1], self.__experiment_label[0]))\n", + " \n", + " if self.__mini_meta is True:\n", + " comparisons.append(\"weighted delta (only for mean difference)\")\n", + "\n", + " for j, g in enumerate(comparisons):\n", + " out.append(\"{}. {}\".format(j+1, g))\n", + "\n", + " resamples_line1 = \"\\n{} resamples \".format(self.__resamples)\n", + " resamples_line2 = \"will be used to generate the effect size bootstraps.\"\n", + " out.append(resamples_line1 + resamples_line2)\n", + "\n", + " return \"\\n\".join(out)\n", + "\n", + "\n", + " # def __variable_name(self):\n", + " # return [k for k,v in locals().items() if v is self]\n", + " #\n", + " # @property\n", + " # def variable_name(self):\n", + " # return self.__variable_name()\n", + " \n", + " @property\n", + " def mean_diff(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`\n", + "\n", + " \"\"\"\n", + " return self.__mean_diff\n", + " \n", + " \n", + " @property \n", + " def median_diff(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__median_diff\n", + " \n", + " \n", + " @property\n", + " def cohens_d(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__cohens_d\n", + " \n", + " \n", + " @property\n", + " def cohens_h(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__cohens_h\n", + "\n", + "\n", + " @property \n", + " def hedges_g(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__hedges_g\n", + " \n", + " \n", + " @property \n", + " def cliffs_delta(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__cliffs_delta\n", + "\n", + "\n", + " @property\n", + " def data(self):\n", + " \"\"\"\n", + " Returns the pandas DataFrame that was passed to `dabest.load()`.\n", + " When `delta2` is True, a new column is added to support the \n", + " function. The name of this new column is indicated by `x`.\n", + " \"\"\"\n", + " return self.__data\n", + "\n", + "\n", + " @property\n", + " def idx(self):\n", + " \"\"\"\n", + " Returns the order of categories that was passed to `dabest.load()`.\n", + " \"\"\"\n", + " return self.__idx\n", + " \n", + "\n", + " @property\n", + " def x1(self):\n", + " \"\"\"\n", + " Returns the first variable declared in x when it is a delta-delta\n", + " case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__x1\n", + "\n", + "\n", + " @property\n", + " def x1_level(self):\n", + " \"\"\"\n", + " Returns the levels of first variable declared in x when it is a \n", + " delta-delta case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__x1_level\n", + "\n", + "\n", + " @property\n", + " def x2(self):\n", + " \"\"\"\n", + " Returns the second variable declared in x when it is a delta-delta\n", + " case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__x2\n", + "\n", + "\n", + " @property\n", + " def experiment(self):\n", + " \"\"\"\n", + " Returns the column name of experiment labels that was passed to \n", + " `dabest.load()` when it is a delta-delta case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__experiment\n", + " \n", + "\n", + " @property\n", + " def experiment_label(self):\n", + " \"\"\"\n", + " Returns the experiment labels in order that was passed to `dabest.load()`\n", + " when it is a delta-delta case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__experiment_label\n", + "\n", + "\n", + " @property\n", + " def delta2(self):\n", + " \"\"\"\n", + " Returns the boolean parameter indicating if this is a delta-delta \n", + " situation.\n", + " \"\"\"\n", + " return self.__delta2\n", + "\n", + "\n", + " @property\n", + " def is_paired(self):\n", + " \"\"\"\n", + " Returns the type of repeated-measures experiment.\n", + " \"\"\"\n", + " return self.__is_paired\n", + "\n", + "\n", + " @property\n", + " def id_col(self):\n", + " \"\"\"\n", + " Returns the id column declared to `dabest.load()`.\n", + " \"\"\"\n", + " return self.__id_col\n", + "\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " The width of the desired confidence interval.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + "\n", + " @property\n", + " def resamples(self):\n", + " \"\"\"\n", + " The number of resamples used to generate the bootstrap.\n", + " \"\"\"\n", + " return self.__resamples\n", + "\n", + "\n", + " @property\n", + " def random_seed(self):\n", + " \"\"\"\n", + " The number used to initialise the numpy random seed generator, ie.\n", + " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", + " \"\"\"\n", + " return self.__random_seed\n", + "\n", + "\n", + " @property\n", + " def x(self):\n", + " \"\"\"\n", + " Returns the x column that was passed to `dabest.load()`, if any.\n", + " When `delta2` is True, `x` returns the name of the new column created \n", + " for the delta-delta situation. To retrieve the 2 variables passed into \n", + " `x` when `delta2` is True, please call `x1` and `x2` instead.\n", + " \"\"\"\n", + " return self.__x\n", + "\n", + "\n", + " @property\n", + " def y(self):\n", + " \"\"\"\n", + " Returns the y column that was passed to `dabest.load()`, if any.\n", + " \"\"\"\n", + " return self.__y\n", + "\n", + "\n", + " @property\n", + " def _xvar(self):\n", + " \"\"\"\n", + " Returns the xvar in dabest.plot_data.\n", + " \"\"\"\n", + " return self.__xvar\n", + "\n", + "\n", + " @property\n", + " def _yvar(self):\n", + " \"\"\"\n", + " Returns the yvar in dabest.plot_data.\n", + " \"\"\"\n", + " return self.__yvar\n", + "\n", + "\n", + " @property\n", + " def _plot_data(self):\n", + " \"\"\"\n", + " Returns the pandas DataFrame used to produce the estimation stats/plots.\n", + " \"\"\"\n", + " return self.__plot_data\n", + "\n", + " \n", + " @property\n", + " def proportional(self):\n", + " \"\"\"\n", + " Returns the proportional parameter class.\n", + " \"\"\"\n", + " return self.__proportional\n", + "\n", + " \n", + " @property\n", + " def mini_meta(self):\n", + " \"\"\"\n", + " Returns the mini_meta boolean parameter.\n", + " \"\"\"\n", + " return self.__mini_meta\n", + "\n", + "\n", + " @property\n", + " def _all_plot_groups(self):\n", + " \"\"\"\n", + " Returns the all plot groups, as indicated via the `idx` keyword.\n", + " \"\"\"\n", + " return self.__all_plot_groups" + ] + }, + { + "cell_type": "markdown", + "id": "c86c0487", + "metadata": {}, + "source": [ + "#### Example: mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d07d58b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good evening!\n", + "The current time is Fri Mar 31 19:41:17 2023.\n", + "\n", + "The unpaired mean difference between control and test is 0.5 [95%CI -0.0412, 1.0].\n", + "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.mean_diff" + ] + }, + { + "cell_type": "markdown", + "id": "cf5ca0a0", + "metadata": {}, + "source": [ + "This is simply the mean of the control group subtracted from\n", + "the mean of the test group.\n", + "\n", + "$$\\text{Mean difference} = \\overline{x}_{Test} - \\overline{x}_{Control}$$\n", + "\n", + "where $\\overline{x}$ is the mean for the group $x$." + ] + }, + { + "cell_type": "markdown", + "id": "8b3b146c", + "metadata": {}, + "source": [ + "#### Example: median_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e9b8635", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\effsize.py:72: UserWarning: Using median as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using a different statistic, such as the mean.\n", + "When plotting, please consider using percetile confidence intervals by specifying `ci_type='percentile'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n", + "\n", + " return func_difference(control, test, np.median, is_paired)\n" + ] + }, + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good afternoon!\n", + "The current time is Thu Mar 30 17:07:33 2023.\n", + "\n", + "The unpaired median difference between control and test is 0.5 [95%CI -0.0758, 0.991].\n", + "The p-value of the two-sided permutation t-test is 0.103, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.median_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.median_diff" + ] + }, + { + "cell_type": "markdown", + "id": "838b2978", + "metadata": {}, + "source": [ + "\n", + "This is the median difference between the control group and the test group.\n", + "\n", + "If the comparison(s) are unpaired, median_diff is computed with the following equation:\n", + "\n", + "\n", + "$$\\text{Median difference} = \\widetilde{x}_{Test} - \\widetilde{x}_{Control}$$\n", + "\n", + "where $\\widetilde{x}$ is the median for the group $x$.\n", + "\n", + "If the comparison(s) are paired, median_diff is computed with the following equation:\n", + "\n", + "$$\\text{Median difference} = \\widetilde{x}_{Test - Control}$$\n", + " \n", + "\n", + "##### Things to note\n", + "\n", + "Using median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead. \n", + "\n", + "When plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying `ci_type = 'percentile'` in .plot(). \n", + "\n", + "For detailed information, please refer to [Issue 129](https://github.com/ACCLAB/DABEST-python/issues/129). \n" + ] + }, + { + "cell_type": "markdown", + "id": "a5324d21", + "metadata": {}, + "source": [ + "#### Example: cohens_d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "748b5c60", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good afternoon!\n", + "The current time is Thu Mar 30 17:07:39 2023.\n", + "\n", + "The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0843, 0.976].\n", + "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.cohens_d.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.cohens_d" + ] + }, + { + "cell_type": "markdown", + "id": "6f66579c", + "metadata": {}, + "source": [ + "\n", + "Cohen's `d` is simply the mean of the control group subtracted from\n", + "the mean of the test group.\n", + "\n", + "If `paired` is None, then the comparison(s) are unpaired; \n", + "otherwise the comparison(s) are paired.\n", + "\n", + "If the comparison(s) are unpaired, Cohen's `d` is computed with the following equation:\n", + "\n", + "\n", + "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{pooled standard deviation}}$$\n", + "\n", + "\n", + "For paired comparisons, Cohen's d is given by\n", + "\n", + "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{average standard deviation}}$$\n", + "\n", + "where $\\overline{x}$ is the mean of the respective group of observations, ${Var}_{x}$ denotes the variance of that group,\n", + "\n", + "\n", + "$$\\text{pooled standard deviation} = \\sqrt{ \\frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} }$$\n", + "\n", + "and\n", + "\n", + "\n", + "$$\\text{average standard deviation} = \\sqrt{ \\frac{{Var}_{control} + {Var}_{test}} {2}}$$\n", + "\n", + "The sample variance (and standard deviation) uses N-1 degrees of freedoms.\n", + "This is an application of [Bessel's correction](https://en.wikipedia.org/wiki/Bessel%27s_correction), and yields the unbiased sample variance.\n", + "\n", + "References:\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "40f4eff9", + "metadata": {}, + "source": [ + "#### Example: cohens_h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f713781c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good evening!\n", + "The current time is Mon Mar 27 00:48:59 2023.\n", + "\n", + "The unpaired Cohen's h between control and test is 0.0 [95%CI -0.613, 0.429].\n", + "The p-value of the two-sided permutation t-test is 0.799, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = randint.rvs(0, 2, size=30, random_state=12345)\n", + "test = randint.rvs(0, 2, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.cohens_h" + ] + }, + { + "cell_type": "markdown", + "id": "9e3e57bd", + "metadata": {}, + "source": [ + "Cohen's *h* uses the information of proportion in the control and test groups to calculate the distance between two proportions.\n", + "\n", + "It can be used to describe the difference between two proportions as \"small\", \"medium\", or \"large\".\n", + "\n", + "It can be used to determine if the difference between two proportions is \"meaningful\".\n", + "\n", + "A directional Cohen's *h* is computed with the following equation:\n", + "\n", + "\n", + "$$h = 2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}$$\n", + "\n", + "For a non-directional Cohen's *h*, the equation is:\n", + "\n", + "$$h = |2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}|$$\n", + "\n", + "References:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "970fb3b2", + "metadata": {}, + "source": [ + "#### Example: hedges_g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26960f9e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good evening!\n", + "The current time is Mon Mar 27 00:50:18 2023.\n", + "\n", + "The unpaired Hedges' g between control and test is 0.465 [95%CI -0.0832, 0.963].\n", + "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.hedges_g" + ] + }, + { + "cell_type": "markdown", + "id": "66c8a83a", + "metadata": {}, + "source": [ + "Hedges' `g` is `cohens_d` corrected for bias via multiplication with the following correction factor:\n", + " \n", + "$$\\frac{ \\Gamma( \\frac{a} {2} )} {\\sqrt{ \\frac{a} {2} } \\times \\Gamma( \\frac{a - 1} {2} )}$$\n", + "\n", + "where\n", + "\n", + "$$a = {n}_{control} + {n}_{test} - 2$$\n", + "\n", + "and $\\Gamma(x)$ is the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function).\n", + "\n", + "\n", + "\n", + "References:\n", + "\n", + "\n", + " \n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "b1cf0080", + "metadata": {}, + "source": [ + "#### Example: cliffs_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce86c76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good evening!\n", + "The current time is Mon Mar 27 00:53:30 2023.\n", + "\n", + "The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0244, 0.533].\n", + "The p-value of the two-sided permutation t-test is 0.061, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.cliffs_delta.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.cliffs_delta" + ] + }, + { + "cell_type": "markdown", + "id": "9661ab37", + "metadata": {}, + "source": [ + "Cliff's delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.\n", + "\n", + "$$\\text{Cliff's delta} = \\frac{\\#({x}_{test} > {x}_{control}) - \\#({x}_{test} < {x}_{control})} {{n}_{Test} \\times {n}_{Control}}$$\n", + " \n", + " \n", + "where $\\#$ denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample. \n", + " \n", + "Cliff's delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from. \n", + "\n", + "References:\n", + "\n", + "\n", + " \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f50106", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class DeltaDelta(object):\n", + " \"\"\"\n", + " A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:\n", + "\n", + "\n", + " $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n", + "\n", + " $$\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}$$\n", + "\n", + "\n", + " where $\\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\\Delta$ is the mean difference between two samples. \n", + "\n", + " A delta-delta value is then calculated as the mean difference between the two primary deltas:\n", + "\n", + "\n", + " $$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", + "\n", + " \"\"\"\n", + " \n", + " def __init__(self, effectsizedataframe, permutation_count,\n", + " ci=95):\n", + "\n", + " import numpy as np\n", + " from numpy import sort as npsort\n", + " from numpy import sqrt, isinf, isnan\n", + " from ._stats_tools import effsize as es\n", + " from ._stats_tools import confint_1group as ci1g\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + "\n", + "\n", + " from string import Template\n", + " import warnings\n", + " \n", + " self.__effsizedf = effectsizedataframe.results\n", + " self.__dabest_obj = effectsizedataframe.dabest_obj\n", + " self.__ci = ci\n", + " self.__resamples = effectsizedataframe.resamples\n", + " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", + " self.__permutation_count = permutation_count\n", + " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", + " self.__control = self.__dabest_obj.experiment_label[0]\n", + " self.__test = self.__dabest_obj.experiment_label[1]\n", + "\n", + "\n", + " # Compute the bootstrap delta-delta and the true dela-delta based on \n", + " # the raw data \n", + " self.__bootstraps_delta_delta = self.__bootstraps[1] - self.__bootstraps[0]\n", + "\n", + " self.__difference = self.__effsizedf[\"difference\"][1] - self.__effsizedf[\"difference\"][0]\n", + "\n", + "\n", + "\n", + " sorted_delta_delta = npsort(self.__bootstraps_delta_delta)\n", + "\n", + " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", + " self.__bootstraps_delta_delta, self.__difference)\n", + " \n", + " self.__jackknives = np.array(ci1g.compute_1group_jackknife(\n", + " self.__bootstraps_delta_delta, \n", + " np.mean))\n", + "\n", + " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", + "\n", + " # Compute BCa intervals.\n", + " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", + " self.__bias_correction, self.__acceleration_value,\n", + " self.__resamples, ci)\n", + " \n", + " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", + "\n", + " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", + " self.__bca_low = sorted_delta_delta[bca_idx_low]\n", + " self.__bca_high = sorted_delta_delta[bca_idx_high]\n", + "\n", + " err1 = \"The $lim_type limit of the interval\"\n", + " err2 = \"was in the $loc 10 values.\"\n", + " err3 = \"The result should be considered unstable.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if bca_idx_low <= 10:\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", + " loc=\"bottom\"),\n", + " stacklevel=1)\n", + "\n", + " if bca_idx_high >= self.__resamples-9:\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", + " loc=\"top\"),\n", + " stacklevel=1)\n", + "\n", + " else:\n", + " err1 = \"The $lim_type limit of the BCa interval cannot be computed.\"\n", + " err2 = \"It is set to the effect size itself.\"\n", + " err3 = \"All bootstrap values were likely all the same.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if isnan(bca_idx_low):\n", + " self.__bca_low = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", + " stacklevel=0)\n", + "\n", + " if isnan(bca_idx_high):\n", + " self.__bca_high = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", + " stacklevel=0)\n", + "\n", + " # Compute percentile intervals.\n", + " pct_idx_low = int((self.__alpha/2) * self.__resamples)\n", + " pct_idx_high = int((1-(self.__alpha/2)) * self.__resamples)\n", + "\n", + " self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", + " self.__pct_low = sorted_delta_delta[pct_idx_low]\n", + " self.__pct_high = sorted_delta_delta[pct_idx_high]\n", + " \n", + " \n", + "\n", + " def __permutation_test(self):\n", + " \"\"\"\n", + " Perform a permutation test and obtain the permutation p-value\n", + " based on the permutation data.\n", + " \"\"\"\n", + " import numpy as np\n", + " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", + "\n", + " THRESHOLD = np.abs(self.__difference)\n", + "\n", + " self.__permutations_delta_delta = np.array(self.__permutations[1]-self.__permutations[0])\n", + "\n", + " count = sum(np.abs(self.__permutations_delta_delta)>THRESHOLD)\n", + " self.__pvalue_permutation = count/self.__permutation_count\n", + "\n", + "\n", + "\n", + " def __repr__(self, header=True, sigfig=3):\n", + " from .__init__ import __version__\n", + " import datetime as dt\n", + " import numpy as np\n", + "\n", + " from .misc_tools import print_greeting\n", + "\n", + " first_line = {\"control\" : self.__control,\n", + " \"test\" : self.__test}\n", + " \n", + " out1 = \"The delta-delta between {control} and {test} \".format(**first_line)\n", + " \n", + " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", + " if \".\" in str(self.__ci):\n", + " ci_width = base_string_fmt.format(self.__ci)\n", + " else:\n", + " ci_width = str(self.__ci)\n", + " \n", + " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", + " \"ci\" : ci_width,\n", + " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", + " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", + " \n", + " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", + " out = out1 + out2\n", + "\n", + " if header is True:\n", + " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", + "\n", + "\n", + " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", + "\n", + " \n", + " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", + " p2 = \"calculated for legacy purposes only. \"\n", + " pvalue = p1 + p2\n", + "\n", + "\n", + " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", + " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", + " bs = bs1 + bs2\n", + "\n", + " pval_def1 = \"Any p-value reported is the probability of observing the \" + \\\n", + " \"effect size (or greater),\\nassuming the null hypothesis of \" + \\\n", + " \"zero difference is true.\"\n", + " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", + " \"control and test labels were performed.\"\n", + " pval_def = pval_def1 + pval_def2\n", + "\n", + "\n", + " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", + "\n", + "\n", + " def to_dict(self):\n", + " \"\"\"\n", + " Returns the attributes of the `DeltaDelta` object as a\n", + " dictionary.\n", + " \"\"\"\n", + " # Only get public (user-facing) attributes.\n", + " attrs = [a for a in dir(self)\n", + " if not a.startswith((\"_\", \"to_dict\"))]\n", + " out = {}\n", + " for a in attrs:\n", + " out[a] = getattr(self, a)\n", + " return out\n", + "\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " Returns the width of the confidence interval, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + "\n", + " @property\n", + " def alpha(self):\n", + " \"\"\"\n", + " Returns the significance level of the statistical test as a float\n", + " between 0 and 1.\n", + " \"\"\"\n", + " return self.__alpha\n", + "\n", + "\n", + " @property\n", + " def bias_correction(self):\n", + " return self.__bias_correction\n", + "\n", + "\n", + " @property\n", + " def bootstraps(self):\n", + " '''\n", + " Return the bootstrapped deltas from all the experiment groups.\n", + " '''\n", + " return self.__bootstraps\n", + "\n", + "\n", + " @property\n", + " def jackknives(self):\n", + " return self.__jackknives\n", + "\n", + "\n", + " @property\n", + " def acceleration_value(self):\n", + " return self.__acceleration_value\n", + "\n", + "\n", + " @property\n", + " def bca_low(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__bca_low\n", + "\n", + "\n", + " @property\n", + " def bca_high(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval upper limit.\n", + " \"\"\"\n", + " return self.__bca_high\n", + "\n", + "\n", + " @property\n", + " def bca_interval_idx(self):\n", + " return self.__bca_interval_idx\n", + "\n", + "\n", + " @property\n", + " def control(self):\n", + " '''\n", + " Return the name of the control experiment group.\n", + " '''\n", + " return self.__control\n", + "\n", + "\n", + " @property\n", + " def test(self):\n", + " '''\n", + " Return the name of the test experiment group.\n", + " '''\n", + " return self.__test\n", + "\n", + "\n", + " @property\n", + " def bootstraps_delta_delta(self):\n", + " '''\n", + " Return the delta-delta values calculated from the bootstrapped \n", + " deltas.\n", + " '''\n", + " return self.__bootstraps_delta_delta\n", + "\n", + "\n", + " @property\n", + " def difference(self):\n", + " '''\n", + " Return the delta-delta value calculated based on the raw data.\n", + " '''\n", + " return self.__difference\n", + "\n", + "\n", + " @property\n", + " def pct_interval_idx (self):\n", + " return self.__pct_interval_idx \n", + "\n", + "\n", + " @property\n", + " def pct_low(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_low\n", + "\n", + "\n", + " @property\n", + " def pct_high(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_high\n", + "\n", + "\n", + " @property\n", + " def pvalue_permutation(self):\n", + " try:\n", + " return self.__pvalue_permutation\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__pvalue_permutation\n", + " \n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__permutation_count\n", + "\n", + " \n", + " @property\n", + " def permutations(self):\n", + " '''\n", + " Return the mean differences of permutations obtained during\n", + " the permutation test for each experiment group.\n", + " '''\n", + " try:\n", + " return self.__permutations\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations\n", + "\n", + " \n", + " @property\n", + " def permutations_delta_delta(self):\n", + " '''\n", + " Return the delta-delta values of permutations obtained \n", + " during the permutation test.\n", + " '''\n", + " try:\n", + " return self.__permutations_delta_delta\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations_delta_delta\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c6a7192f", + "metadata": {}, + "source": [ + "\n", + "\n", + "and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", + "\n", + "\n", + "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", + "\n", + "where $s$ is the standard deviation and $n$ is the sample size." + ] + }, + { + "cell_type": "markdown", + "id": "a5905b79", + "metadata": {}, + "source": [ + "#### Example: delta-delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "088f734b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "N = 20\n", + "# Create samples\n", + "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", + "y[N:2*N] = y[N:2*N]+1\n", + "y[2*N:3*N] = y[2*N:3*N]-0.5\n", + "# Add a `Treatment` column\n", + "t1 = np.repeat('Placebo', N*2).tolist()\n", + "t2 = np.repeat('Drug', N*2).tolist()\n", + "treatment = t1 + t2 \n", + "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", + "rep = []\n", + "for i in range(N*2):\n", + " rep.append('Rep1')\n", + " rep.append('Rep2')\n", + "# Add a `Genotype` column as the second variable\n", + "wt = np.repeat('W', N).tolist()\n", + "mt = np.repeat('M', N).tolist()\n", + "wt2 = np.repeat('W', N).tolist()\n", + "mt2 = np.repeat('M', N).tolist()\n", + "genotype = wt + mt + wt2 + mt2\n", + "# Add an `id` column for paired data plotting.\n", + "id = list(range(0, N*2))\n", + "id_col = id + id \n", + "# Combine all columns into a DataFrame.\n", + "df_delta2 = pd.DataFrame({'ID' : id_col,\n", + " 'Rep' : rep,\n", + " 'Genotype' : genotype, \n", + " 'Treatment': treatment,\n", + " 'Y' : y\n", + " })\n", + "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", + "unpaired_delta2.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24c4b036", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class MiniMetaDelta(object):\n", + " \"\"\"\n", + " A class to compute and store the weighted delta.\n", + " A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``.\n", + " \n", + " \"\"\"\n", + "\n", + " def __init__(self, effectsizedataframe, permutation_count,\n", + " ci=95):\n", + "\n", + " import numpy as np\n", + " from numpy import sort as npsort\n", + " from numpy import sqrt, isinf, isnan\n", + " from ._stats_tools import effsize as es\n", + " from ._stats_tools import confint_1group as ci1g\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + "\n", + "\n", + " from string import Template\n", + " import warnings\n", + " \n", + " self.__effsizedf = effectsizedataframe.results\n", + " self.__dabest_obj = effectsizedataframe.dabest_obj\n", + " self.__ci = ci\n", + " self.__resamples = effectsizedataframe.resamples\n", + " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", + " self.__permutation_count = permutation_count\n", + " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", + " self.__control = np.array(self.__effsizedf[\"control\"])\n", + " self.__test = np.array(self.__effsizedf[\"test\"])\n", + " self.__control_N = np.array(self.__effsizedf[\"control_N\"])\n", + " self.__test_N = np.array(self.__effsizedf[\"test_N\"])\n", + "\n", + "\n", + " idx = self.__dabest_obj.idx\n", + " dat = self.__dabest_obj._plot_data\n", + " xvar = self.__dabest_obj._xvar\n", + " yvar = self.__dabest_obj._yvar\n", + "\n", + " # compute the variances of each control group and each test group\n", + " control_var=[]\n", + " test_var=[]\n", + " for j, current_tuple in enumerate(idx):\n", + " cname = current_tuple[0]\n", + " control = dat[dat[xvar] == cname][yvar].copy()\n", + " control_var.append(np.var(control, ddof=1))\n", + "\n", + " tname = current_tuple[1]\n", + " test = dat[dat[xvar] == tname][yvar].copy()\n", + " test_var.append(np.var(test, ddof=1))\n", + " self.__control_var = np.array(control_var)\n", + " self.__test_var = np.array(test_var)\n", + "\n", + " # Compute pooled group variances for each pair of experiment groups\n", + " # based on the raw data\n", + " self.__group_var = ci2g.calculate_group_var(self.__control_var, \n", + " self.__control_N,\n", + " self.__test_var, \n", + " self.__test_N)\n", + "\n", + " # Compute the weighted average mean differences of the bootstrap data\n", + " # using the pooled group variances of the raw data as the inverse of \n", + " # weights\n", + " self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(\n", + " self.__group_var, \n", + " self.__bootstraps, \n", + " self.__resamples)\n", + "\n", + " # Compute the weighted average mean difference based on the raw data\n", + " self.__difference = es.weighted_delta(self.__effsizedf[\"difference\"],\n", + " self.__group_var)\n", + "\n", + " sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)\n", + "\n", + "\n", + " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", + " self.__bootstraps_weighted_delta, self.__difference)\n", + " \n", + " self.__jackknives = np.array(ci1g.compute_1group_jackknife(\n", + " self.__bootstraps_weighted_delta, \n", + " np.mean))\n", + "\n", + " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", + "\n", + " # Compute BCa intervals.\n", + " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", + " self.__bias_correction, self.__acceleration_value,\n", + " self.__resamples, ci)\n", + " \n", + " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", + "\n", + " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", + " self.__bca_low = sorted_weighted_deltas[bca_idx_low]\n", + " self.__bca_high = sorted_weighted_deltas[bca_idx_high]\n", + "\n", + " err1 = \"The $lim_type limit of the interval\"\n", + " err2 = \"was in the $loc 10 values.\"\n", + " err3 = \"The result should be considered unstable.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if bca_idx_low <= 10:\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", + " loc=\"bottom\"),\n", + " stacklevel=1)\n", + "\n", + " if bca_idx_high >= self.__resamples-9:\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", + " loc=\"top\"),\n", + " stacklevel=1)\n", + "\n", + " else:\n", + " err1 = \"The $lim_type limit of the BCa interval cannot be computed.\"\n", + " err2 = \"It is set to the effect size itself.\"\n", + " err3 = \"All bootstrap values were likely all the same.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if isnan(bca_idx_low):\n", + " self.__bca_low = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", + " stacklevel=0)\n", + "\n", + " if isnan(bca_idx_high):\n", + " self.__bca_high = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", + " stacklevel=0)\n", + "\n", + " # Compute percentile intervals.\n", + " pct_idx_low = int((self.__alpha/2) * self.__resamples)\n", + " pct_idx_high = int((1-(self.__alpha/2)) * self.__resamples)\n", + "\n", + " self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", + " self.__pct_low = sorted_weighted_deltas[pct_idx_low]\n", + " self.__pct_high = sorted_weighted_deltas[pct_idx_high]\n", + " \n", + " \n", + "\n", + " def __permutation_test(self):\n", + " \"\"\"\n", + " Perform a permutation test and obtain the permutation p-value\n", + " based on the permutation data.\n", + " \"\"\"\n", + " import numpy as np\n", + " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", + " self.__permutations_var = np.array(self.__effsizedf[\"permutations_var\"])\n", + "\n", + " THRESHOLD = np.abs(self.__difference)\n", + "\n", + " all_num = []\n", + " all_denom = []\n", + "\n", + " groups = len(self.__permutations)\n", + " for i in range(0, len(self.__permutations[0])):\n", + " weight = [1/self.__permutations_var[j][i] for j in range(0, groups)]\n", + " all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)]))\n", + " all_denom.append(np.sum(weight))\n", + " \n", + " output=[]\n", + " for i in range(0, len(all_num)):\n", + " output.append(all_num[i]/all_denom[i])\n", + " \n", + " self.__permutations_weighted_delta = np.array(output)\n", + "\n", + " count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD)\n", + " self.__pvalue_permutation = count/self.__permutation_count\n", + "\n", + "\n", + "\n", + " def __repr__(self, header=True, sigfig=3):\n", + " from .__init__ import __version__\n", + " import datetime as dt\n", + " import numpy as np\n", + "\n", + " from .misc_tools import print_greeting\n", + " \n", + " is_paired = self.__dabest_obj.is_paired\n", + "\n", + " PAIRED_STATUS = {'baseline' : 'paired', \n", + " 'sequential' : 'paired',\n", + " 'None' : 'unpaired'\n", + " }\n", + "\n", + " first_line = {\"paired_status\": PAIRED_STATUS[str(is_paired)]}\n", + " \n", + "\n", + " out1 = \"The weighted-average {paired_status} mean differences \".format(**first_line)\n", + " \n", + " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", + " if \".\" in str(self.__ci):\n", + " ci_width = base_string_fmt.format(self.__ci)\n", + " else:\n", + " ci_width = str(self.__ci)\n", + " \n", + " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", + " \"ci\" : ci_width,\n", + " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", + " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", + " \n", + " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", + " out = out1 + out2\n", + "\n", + " if header is True:\n", + " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", + "\n", + "\n", + " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", + "\n", + " \n", + " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", + " p2 = \"calculated for legacy purposes only. \"\n", + " pvalue = p1 + p2\n", + "\n", + "\n", + " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", + " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", + " bs = bs1 + bs2\n", + "\n", + " pval_def1 = \"Any p-value reported is the probability of observing the\" + \\\n", + " \"effect size (or greater),\\nassuming the null hypothesis of\" + \\\n", + " \"zero difference is true.\"\n", + " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", + " \"control and test labels were performed.\"\n", + " pval_def = pval_def1 + pval_def2\n", + "\n", + "\n", + " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", + "\n", + "\n", + " def to_dict(self):\n", + " \"\"\"\n", + " Returns all attributes of the `dabest.MiniMetaDelta` object as a\n", + " dictionary.\n", + " \"\"\"\n", + " # Only get public (user-facing) attributes.\n", + " attrs = [a for a in dir(self)\n", + " if not a.startswith((\"_\", \"to_dict\"))]\n", + " out = {}\n", + " for a in attrs:\n", + " out[a] = getattr(self, a)\n", + " return out\n", + "\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " Returns the width of the confidence interval, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + "\n", + " @property\n", + " def alpha(self):\n", + " \"\"\"\n", + " Returns the significance level of the statistical test as a float\n", + " between 0 and 1.\n", + " \"\"\"\n", + " return self.__alpha\n", + "\n", + "\n", + " @property\n", + " def bias_correction(self):\n", + " return self.__bias_correction\n", + "\n", + "\n", + " @property\n", + " def bootstraps(self):\n", + " '''\n", + " Return the bootstrapped differences from all the experiment groups.\n", + " '''\n", + " return self.__bootstraps\n", + "\n", + "\n", + " @property\n", + " def jackknives(self):\n", + " return self.__jackknives\n", + "\n", + "\n", + " @property\n", + " def acceleration_value(self):\n", + " return self.__acceleration_value\n", + "\n", + "\n", + " @property\n", + " def bca_low(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__bca_low\n", + "\n", + "\n", + " @property\n", + " def bca_high(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval upper limit.\n", + " \"\"\"\n", + " return self.__bca_high\n", + "\n", + "\n", + " @property\n", + " def bca_interval_idx(self):\n", + " return self.__bca_interval_idx\n", + "\n", + "\n", + " @property\n", + " def control(self):\n", + " '''\n", + " Return the names of the control groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__control\n", + "\n", + "\n", + " @property\n", + " def test(self):\n", + " '''\n", + " Return the names of the test groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__test\n", + " \n", + " @property\n", + " def control_N(self):\n", + " '''\n", + " Return the sizes of the control groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__control_N\n", + "\n", + "\n", + " @property\n", + " def test_N(self):\n", + " '''\n", + " Return the sizes of the test groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__test_N\n", + "\n", + "\n", + " @property\n", + " def control_var(self):\n", + " '''\n", + " Return the estimated population variances of the control groups \n", + " from all the experiment groups in order. Here the population \n", + " variance is estimated from the sample variance. \n", + " '''\n", + " return self.__control_var\n", + "\n", + "\n", + " @property\n", + " def test_var(self):\n", + " '''\n", + " Return the estimated population variances of the control groups \n", + " from all the experiment groups in order. Here the population \n", + " variance is estimated from the sample variance. \n", + " '''\n", + " return self.__test_var\n", + "\n", + " \n", + " @property\n", + " def group_var(self):\n", + " '''\n", + " Return the pooled group variances of all the experiment groups \n", + " in order. \n", + " '''\n", + " return self.__group_var\n", + "\n", + "\n", + " @property\n", + " def bootstraps_weighted_delta(self):\n", + " '''\n", + " Return the weighted-average mean differences calculated from the bootstrapped \n", + " deltas and weights across the experiment groups, where the weights are \n", + " the inverse of the pooled group variances.\n", + " '''\n", + " return self.__bootstraps_weighted_delta\n", + "\n", + "\n", + " @property\n", + " def difference(self):\n", + " '''\n", + " Return the weighted-average delta calculated from the raw data.\n", + " '''\n", + " return self.__difference\n", + "\n", + "\n", + " @property\n", + " def pct_interval_idx (self):\n", + " return self.__pct_interval_idx \n", + "\n", + "\n", + " @property\n", + " def pct_low(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_low\n", + "\n", + "\n", + " @property\n", + " def pct_high(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_high\n", + "\n", + "\n", + " @property\n", + " def pvalue_permutation(self):\n", + " try:\n", + " return self.__pvalue_permutation\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__pvalue_permutation\n", + " \n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__permutation_count\n", + "\n", + " \n", + " @property\n", + " def permutations(self):\n", + " '''\n", + " Return the mean differences of permutations obtained during\n", + " the permutation test for each experiment group.\n", + " '''\n", + " try:\n", + " return self.__permutations\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations\n", + "\n", + "\n", + " @property\n", + " def permutations_var(self):\n", + " '''\n", + " Return the pooled group variances of permutations obtained during\n", + " the permutation test for each experiment group.\n", + " '''\n", + " try:\n", + " return self.__permutations_var\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations_var\n", + "\n", + " \n", + " @property\n", + " def permutations_weighted_delta(self):\n", + " '''\n", + " Return the weighted-average deltas of permutations obtained \n", + " during the permutation test.\n", + " '''\n", + " try:\n", + " return self.__permutations_weighted_delta\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations_weighted_delta\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae5bac56", + "metadata": {}, + "source": [ + "The weighted delta is calcuated as follows:\n", + "\n", + "$$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$$\n", + "\n", + "where:\n", + "\n", + "$$\\hat{\\theta_{i}} = \\text{Mean difference for replicate }i$$\n", + "\n", + "\n", + "$$w_{i} = \\text{Weight for replicate }i = \\frac{1}{s_{i}^2} $$\n", + "\n", + "$$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$$\n", + "\n", + "$$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "dc1239ee", + "metadata": {}, + "source": [ + "#### Example: mini-meta-delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e144ed50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good morning!\n", + "The current time is Mon Mar 27 01:01:11 2023.\n", + "\n", + "The weighted-average unpaired mean differences is 0.0336 [95%CI -0.137, 0.228].\n", + "The p-value of the two-sided permutation t-test is 0.736, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Ns = 20\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "my_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3})\n", + "my_dabest_object = dabest.load(my_df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\n", + "my_dabest_object.mean_diff.mini_meta_delta" + ] + }, + { + "cell_type": "markdown", + "id": "669285cb", + "metadata": {}, + "source": [ + "As of version 2023.02.14, weighted delta can only be calculated for mean difference, and not for standardized measures such as Cohen's *d*.\n", + "\n", + "Details about the calculated weighted delta are accessed as attributes of the ``mini_meta_delta`` class. See the `minimetadelta` for details on usage.\n", + "\n", + "Refer to Chapter 10 of the Cochrane handbook for further information on meta-analysis: \n", + "https://training.cochrane.org/handbook/current/chapter-10\n", + "\t\t" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6017e0d4", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class TwoGroupsEffectSize(object):\n", + "\n", + " \"\"\"\n", + " A class to compute and store the results of bootstrapped\n", + " mean differences between two groups.\n", + " \n", + " Compute the effect size between two groups.\n", + "\n", + " Parameters\n", + " ----------\n", + " control : array-like\n", + " test : array-like\n", + " These should be numerical iterables.\n", + " effect_size : string.\n", + " Any one of the following are accepted inputs:\n", + " 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", + " is_paired : string, default None\n", + " resamples : int, default 5000\n", + " The number of bootstrap resamples to be taken for the calculation\n", + " of the confidence interval limits.\n", + " permutation_count : int, default 5000\n", + " The number of permutations (reshuffles) to perform for the \n", + " computation of the permutation p-value\n", + " ci : float, default 95\n", + " The confidence interval width. The default of 95 produces 95%\n", + " confidence intervals.\n", + " random_seed : int, default 12345\n", + " `random_seed` is used to seed the random number generator during\n", + " bootstrap resampling. This ensures that the confidence intervals\n", + " reported are replicable.\n", + "\n", + " Returns\n", + " -------\n", + " A :py:class:`TwoGroupEffectSize` object:\n", + " `difference` : float\n", + " The effect size of the difference between the control and the test.\n", + " `effect_size` : string\n", + " The type of effect size reported.\n", + " `is_paired` : string\n", + " The type of repeated-measures experiment.\n", + " `ci` : float\n", + " Returns the width of the confidence interval, in percent.\n", + " `alpha` : float\n", + " Returns the significance level of the statistical test as a float between 0 and 1.\n", + " `resamples` : int\n", + " The number of resamples performed during the bootstrap procedure.\n", + " `bootstraps` : numpy ndarray\n", + " The generated bootstraps of the effect size.\n", + " `random_seed` : int\n", + " The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned.\n", + " `bca_low, bca_high` : float\n", + " The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively.\n", + " `pct_low, pct_high` : float\n", + " The percentile confidence interval lower limit and upper limits, respectively.\n", + " \"\"\"\n", + "\n", + " def __init__(self, control, test, effect_size,\n", + " proportional=False,\n", + " is_paired=None, ci=95,\n", + " resamples=5000, \n", + " permutation_count=5000, \n", + " random_seed=12345):\n", + "\n", + " \n", + " import numpy as np\n", + " from numpy import array, isnan, isinf\n", + " from numpy import sort as npsort\n", + " from numpy.random import choice, seed\n", + "\n", + " import scipy.stats as spstats\n", + "\n", + " # import statsmodels.stats.power as power\n", + " import statsmodels\n", + "\n", + " from string import Template\n", + " import warnings\n", + " \n", + " from ._stats_tools import effsize as es\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + "\n", + "\n", + " self.__EFFECT_SIZE_DICT = {\"mean_diff\" : \"mean difference\",\n", + " \"median_diff\" : \"median difference\",\n", + " \"cohens_d\" : \"Cohen's d\",\n", + " \"cohens_h\" : \"Cohen's h\",\n", + " \"hedges_g\" : \"Hedges' g\",\n", + " \"cliffs_delta\" : \"Cliff's delta\"}\n", + "\n", + "\n", + " kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()]\n", + " if effect_size not in kosher_es:\n", + " err1 = \"The effect size '{}'\".format(effect_size)\n", + " err2 = \"is not one of {}\".format(kosher_es)\n", + " raise ValueError(\" \".join([err1, err2]))\n", + "\n", + " if effect_size == \"cliffs_delta\" and is_paired:\n", + " err1 = \"`paired` is not None; therefore Cliff's delta is not defined.\"\n", + " raise ValueError(err1)\n", + "\n", + " if proportional==True and effect_size not in ['mean_diff','cohens_h']:\n", + " err1 = \"`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined.\"\n", + " raise ValueError(err1)\n", + "\n", + " if proportional==True and (np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False):\n", + " err1 = \"`proportional` is True; Only accept binary data consisting of 0 and 1.\"\n", + " raise ValueError(err1)\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " control = array(control)\n", + " test = array(test)\n", + " control = control[~isnan(control)]\n", + " test = test[~isnan(test)]\n", + "\n", + " self.__effect_size = effect_size\n", + " self.__control = control\n", + " self.__test = test\n", + " self.__is_paired = is_paired\n", + " self.__resamples = resamples\n", + " self.__permutation_count = permutation_count\n", + " self.__random_seed = random_seed\n", + " self.__ci = ci\n", + " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", + "\n", + " self.__difference = es.two_group_difference(\n", + " control, test, is_paired, effect_size)\n", + " \n", + " self.__jackknives = ci2g.compute_meandiff_jackknife(\n", + " control, test, is_paired, effect_size)\n", + "\n", + " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", + "\n", + " bootstraps = ci2g.compute_bootstrapped_diff(\n", + " control, test, is_paired, effect_size,\n", + " resamples, random_seed)\n", + " self.__bootstraps = bootstraps\n", + " \n", + " sorted_bootstraps = npsort(self.__bootstraps)\n", + " # Added in v0.2.6.\n", + " # Raises a UserWarning if there are any infiinities in the bootstraps.\n", + " num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)])\n", + " \n", + " if num_infinities > 0:\n", + " warn_msg = \"There are {} bootstrap(s) that are not defined. \"\\\n", + " \"This is likely due to smaple sample sizes. \"\\\n", + " \"The values in a bootstrap for a group will be more likely \"\\\n", + " \"to be all equal, with a resulting variance of zero. \"\\\n", + " \"The computation of Cohen's d and Hedges' g thus \"\\\n", + " \"involved a division by zero. \"\n", + " warnings.warn(warn_msg.format(num_infinities), \n", + " category=UserWarning)\n", + "\n", + " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", + " self.__bootstraps, self.__difference)\n", + "\n", + " # Compute BCa intervals.\n", + " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", + " self.__bias_correction, self.__acceleration_value,\n", + " self.__resamples, ci)\n", + "\n", + " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", + "\n", + " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", + " self.__bca_low = sorted_bootstraps[bca_idx_low]\n", + " self.__bca_high = sorted_bootstraps[bca_idx_high]\n", + "\n", + " err1 = \"The $lim_type limit of the interval\"\n", + " err2 = \"was in the $loc 10 values.\"\n", + " err3 = \"The result should be considered unstable.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if bca_idx_low <= 10:\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", + " loc=\"bottom\"),\n", + " stacklevel=1)\n", + "\n", + " if bca_idx_high >= resamples-9:\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", + " loc=\"top\"),\n", + " stacklevel=1)\n", + "\n", + " else:\n", + " err1 = \"The $lim_type limit of the BCa interval cannot be computed.\"\n", + " err2 = \"It is set to the effect size itself.\"\n", + " err3 = \"All bootstrap values were likely all the same.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if isnan(bca_idx_low):\n", + " self.__bca_low = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", + " stacklevel=0)\n", + "\n", + " if isnan(bca_idx_high):\n", + " self.__bca_high = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", + " stacklevel=0)\n", + "\n", + " # Compute percentile intervals.\n", + " pct_idx_low = int((self.__alpha/2) * resamples)\n", + " pct_idx_high = int((1-(self.__alpha/2)) * resamples)\n", + "\n", + " self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", + " self.__pct_low = sorted_bootstraps[pct_idx_low]\n", + " self.__pct_high = sorted_bootstraps[pct_idx_high]\n", + "\n", + " # Perform statistical tests.\n", + " \n", + " self.__PermutationTest_result = PermutationTest(control, test, \n", + " effect_size, \n", + " is_paired,\n", + " permutation_count)\n", + " \n", + " if is_paired and proportional is False:\n", + " # Wilcoxon, a non-parametric version of the paired T-test.\n", + " wilcoxon = spstats.wilcoxon(control, test)\n", + " self.__pvalue_wilcoxon = wilcoxon.pvalue\n", + " self.__statistic_wilcoxon = wilcoxon.statistic\n", + " \n", + " \n", + " if effect_size != \"median_diff\":\n", + " # Paired Student's t-test.\n", + " paired_t = spstats.ttest_rel(control, test, nan_policy='omit')\n", + " self.__pvalue_paired_students_t = paired_t.pvalue\n", + " self.__statistic_paired_students_t = paired_t.statistic\n", + "\n", + " standardized_es = es.cohens_d(control, test, is_paired)\n", + " # self.__power = power.tt_solve_power(standardized_es,\n", + " # len(control),\n", + " # alpha=self.__alpha)\n", + "\n", + " elif is_paired and proportional is True:\n", + " # for binary paired data, use McNemar's test\n", + " # References:\n", + " # https://en.wikipedia.org/wiki/McNemar%27s_test\n", + " from statsmodels.stats.contingency_tables import mcnemar\n", + " import pandas as pd\n", + " df_temp = pd.DataFrame({'control': control, 'test': test})\n", + " x1 = len(df_temp[(df_temp['control'] == 0)&(df_temp['test'] == 0)])\n", + " x2 = len(df_temp[(df_temp['control'] == 0)&(df_temp['test'] == 1)])\n", + " x3 = len(df_temp[(df_temp['control'] == 1)&(df_temp['test'] == 0)])\n", + " x4 = len(df_temp[(df_temp['control'] == 1)&(df_temp['test'] == 1)])\n", + " table = [[x1,x2],[x3,x4]]\n", + " _mcnemar = mcnemar(table, exact=True, correction=True)\n", + " self.__pvalue_mcnemar = _mcnemar.pvalue\n", + " self.__statistic_mcnemar = _mcnemar.statistic\n", + "\n", + " elif effect_size == \"cliffs_delta\":\n", + " # Let's go with Brunner-Munzel!\n", + " brunner_munzel = spstats.brunnermunzel(control, test,\n", + " nan_policy='omit')\n", + " self.__pvalue_brunner_munzel = brunner_munzel.pvalue\n", + " self.__statistic_brunner_munzel = brunner_munzel.statistic\n", + "\n", + "\n", + " elif effect_size == \"median_diff\":\n", + " # According to scipy's documentation of the function,\n", + " # \"The Kruskal-Wallis H-test tests the null hypothesis\n", + " # that the population median of all of the groups are equal.\"\n", + " kruskal = spstats.kruskal(control, test, nan_policy='omit')\n", + " self.__pvalue_kruskal = kruskal.pvalue\n", + " self.__statistic_kruskal = kruskal.statistic\n", + " # self.__power = np.nan\n", + "\n", + " else: # for mean difference, Cohen's d, and Hedges' g.\n", + " # Welch's t-test, assumes normality of distributions,\n", + " # but does not assume equal variances.\n", + " welch = spstats.ttest_ind(control, test, equal_var=False,\n", + " nan_policy='omit')\n", + " self.__pvalue_welch = welch.pvalue\n", + " self.__statistic_welch = welch.statistic\n", + "\n", + " # Student's t-test, assumes normality of distributions,\n", + " # as well as assumption of equal variances.\n", + " students_t = spstats.ttest_ind(control, test, equal_var=True,\n", + " nan_policy='omit')\n", + " self.__pvalue_students_t = students_t.pvalue\n", + " self.__statistic_students_t = students_t.statistic\n", + "\n", + " # Mann-Whitney test: Non parametric,\n", + " # does not assume normality of distributions\n", + " try:\n", + " mann_whitney = spstats.mannwhitneyu(control, test, \n", + " alternative='two-sided')\n", + " self.__pvalue_mann_whitney = mann_whitney.pvalue\n", + " self.__statistic_mann_whitney = mann_whitney.statistic\n", + " except ValueError:\n", + " # Occurs when the control and test are exactly identical\n", + " # in terms of rank (eg. all zeros.)\n", + " pass\n", + " \n", + " \n", + "\n", + " standardized_es = es.cohens_d(control, test, is_paired = None)\n", + " \n", + " # The Cohen's h calculation is for binary categorical data\n", + " try:\n", + " self.__proportional_difference = es.cohens_h(control, test)\n", + " except ValueError:\n", + " # Occur only when the data consists not only 0's and 1's.\n", + " pass\n", + " # self.__power = power.tt_ind_solve_power(standardized_es,\n", + " # len(control),\n", + " # alpha=self.__alpha,\n", + " # ratio=len(test)/len(control)\n", + " # )\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3):\n", + " \n", + " # # Deprecated in v0.3.0; permutation p-values will be reported by default.\n", + " # UNPAIRED_ES_TO_TEST = {\"mean_diff\" : \"Mann-Whitney\",\n", + " # \"median_diff\" : \"Kruskal\",\n", + " # \"cohens_d\" : \"Mann-Whitney\",\n", + " # \"hedges_g\" : \"Mann-Whitney\",\n", + " # \"cliffs_delta\" : \"Brunner-Munzel\"}\n", + " # \n", + " # TEST_TO_PVAL_ATTR = {\"Mann-Whitney\" : \"pvalue_mann_whitney\",\n", + " # \"Kruskal\" : \"pvalue_kruskal\",\n", + " # \"Brunner-Munzel\" : \"pvalue_brunner_munzel\",\n", + " # \"Wilcoxon\" : \"pvalue_wilcoxon\"}\n", + " \n", + " RM_STATUS = {'baseline' : 'for repeated measures against baseline \\n', \n", + " 'sequential': 'for the sequential design of repeated-measures experiment \\n',\n", + " 'None' : ''\n", + " }\n", + "\n", + " PAIRED_STATUS = {'baseline' : 'paired', \n", + " 'sequential' : 'paired',\n", + " 'None' : 'unpaired'\n", + " }\n", + "\n", + " first_line = {\"rm_status\" : RM_STATUS[str(self.__is_paired)],\n", + " \"es\" : self.__EFFECT_SIZE_DICT[self.__effect_size],\n", + " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)]}\n", + " \n", + "\n", + " out1 = \"The {paired_status} {es} {rm_status}\".format(**first_line)\n", + " \n", + " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", + " if \".\" in str(self.__ci):\n", + " ci_width = base_string_fmt.format(self.__ci)\n", + " else:\n", + " ci_width = str(self.__ci)\n", + " \n", + " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", + " \"ci\" : ci_width,\n", + " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", + " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", + " \n", + " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", + " out = out1 + out2\n", + " \n", + " # # Deprecated in v0.3.0; permutation p-values will be reported by default.\n", + " # if self.__is_paired:\n", + " # stats_test = \"Wilcoxon\"\n", + " # else:\n", + " # stats_test = UNPAIRED_ES_TO_TEST[self.__effect_size]\n", + " \n", + " \n", + " # pval_rounded = base_string_fmt.format(getattr(self,\n", + " # TEST_TO_PVAL_ATTR[stats_test])\n", + " # )\n", + " \n", + " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", + " \n", + " # # Deprecated in v0.3.0; permutation p-values will be reported by default.\n", + " # pvalue = \"The two-sided p-value of the {} test is {}.\".format(stats_test,\n", + " # pval_rounded)\n", + " \n", + " # pvalue = \"The two-sided p-value of the {} test is {}.\".format(stats_test,\n", + " # pval_rounded)\n", + " \n", + " \n", + " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", + " p2 = \"calculated for legacy purposes only. \"\n", + " pvalue = p1 + p2\n", + " \n", + " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", + " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", + " bs = bs1 + bs2\n", + "\n", + " pval_def1 = \"Any p-value reported is the probability of observing the\" + \\\n", + " \"effect size (or greater),\\nassuming the null hypothesis of\" + \\\n", + " \"zero difference is true.\"\n", + " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", + " \"control and test labels were performed.\"\n", + " pval_def = pval_def1 + pval_def2\n", + "\n", + " if show_resample_count and define_pval:\n", + " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", + " elif show_resample_count is False and define_pval is True:\n", + " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, pval_def)\n", + " elif show_resample_count is True and define_pval is False:\n", + " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, bs)\n", + " else:\n", + " return \"{}\\n{}\".format(out, pvalue)\n", + "\n", + "\n", + "\n", + " def to_dict(self):\n", + " \"\"\"\n", + " Returns the attributes of the `dabest.TwoGroupEffectSize` object as a\n", + " dictionary.\n", + " \"\"\"\n", + " # Only get public (user-facing) attributes.\n", + " attrs = [a for a in dir(self)\n", + " if not a.startswith((\"_\", \"to_dict\"))]\n", + " out = {}\n", + " for a in attrs:\n", + " out[a] = getattr(self, a)\n", + " return out\n", + "\n", + "\n", + " @property\n", + " def difference(self):\n", + " \"\"\"\n", + " Returns the difference between the control and the test.\n", + " \"\"\"\n", + " return self.__difference\n", + "\n", + " @property\n", + " def effect_size(self):\n", + " \"\"\"\n", + " Returns the type of effect size reported.\n", + " \"\"\"\n", + " return self.__EFFECT_SIZE_DICT[self.__effect_size]\n", + "\n", + " @property\n", + " def is_paired(self):\n", + " return self.__is_paired\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " Returns the width of the confidence interval, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + " @property\n", + " def alpha(self):\n", + " \"\"\"\n", + " Returns the significance level of the statistical test as a float\n", + " between 0 and 1.\n", + " \"\"\"\n", + " return self.__alpha\n", + "\n", + " @property\n", + " def resamples(self):\n", + " \"\"\"\n", + " The number of resamples performed during the bootstrap procedure.\n", + " \"\"\"\n", + " return self.__resamples\n", + "\n", + " @property\n", + " def bootstraps(self):\n", + " \"\"\"\n", + " The generated bootstraps of the effect size.\n", + " \"\"\"\n", + " return self.__bootstraps\n", + "\n", + " @property\n", + " def random_seed(self):\n", + " \"\"\"\n", + " The number used to initialise the numpy random seed generator, ie.\n", + " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", + " \"\"\"\n", + " return self.__random_seed\n", + "\n", + " @property\n", + " def bca_interval_idx(self):\n", + " return self.__bca_interval_idx\n", + "\n", + " @property\n", + " def bca_low(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__bca_low\n", + "\n", + " @property\n", + " def bca_high(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval upper limit.\n", + " \"\"\"\n", + " return self.__bca_high\n", + "\n", + " @property\n", + " def pct_interval_idx(self):\n", + " return self.__pct_interval_idx\n", + "\n", + " @property\n", + " def pct_low(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_low\n", + "\n", + " @property\n", + " def pct_high(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_high\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_brunner_munzel(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_brunner_munzel\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_brunner_munzel(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_brunner_munzel\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_wilcoxon(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_wilcoxon\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_wilcoxon(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_wilcoxon\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_mcnemar(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_mcnemar\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_mcnemar(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_mcnemar\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_paired_students_t(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_paired_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_paired_students_t(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_paired_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_kruskal(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_kruskal\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_kruskal(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_kruskal\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_welch(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_welch\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_welch(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_welch\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_students_t(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_students_t(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def pvalue_mann_whitney(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__pvalue_mann_whitney\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + "\n", + "\n", + " @property\n", + " def statistic_mann_whitney(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__statistic_mann_whitney\n", + " except AttributeError:\n", + " return npnan\n", + " \n", + " # Introduced in v0.3.0.\n", + " @property\n", + " def pvalue_permutation(self):\n", + " return self.__PermutationTest_result.pvalue\n", + " \n", + " # \n", + " # \n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__PermutationTest_result.permutation_count\n", + "\n", + " \n", + " @property\n", + " def permutations(self):\n", + " return self.__PermutationTest_result.permutations\n", + "\n", + " \n", + " @property\n", + " def permutations_var(self):\n", + " return self.__PermutationTest_result.permutations_var\n", + "\n", + "\n", + " @property\n", + " def proportional_difference(self):\n", + " from numpy import nan as npnan\n", + " try:\n", + " return self.__proportional_difference\n", + " except AttributeError:\n", + " return npnan\n" + ] + }, + { + "cell_type": "markdown", + "id": "d72ccb04", + "metadata": {}, + "source": [ + "#### Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d8a7a87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "The unpaired mean difference is -0.253 [95%CI -0.78, 0.25].\n", + "The p-value of the two-sided permutation t-test is 0.348, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(12345)\n", + "control = norm.rvs(loc=0, size=30)\n", + "test = norm.rvs(loc=0.5, size=30)\n", + "effsize = dabest.TwoGroupsEffectSize(control, test, \"mean_diff\")\n", + "effsize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72a4c93e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'alpha': 0.05,\n", + " 'bca_high': 0.24951887238295106,\n", + " 'bca_interval_idx': (125, 4875),\n", + " 'bca_low': -0.7801782111071534,\n", + " 'bootstraps': array([-0.3649424 , -0.45018155, -0.56034412, ..., -0.49805581,\n", + " -0.25334475, -0.55206229]),\n", + " 'ci': 95,\n", + " 'difference': -0.25315417702752846,\n", + " 'effect_size': 'mean difference',\n", + " 'is_paired': None,\n", + " 'pct_high': 0.24951887238295106,\n", + " 'pct_interval_idx': (125, 4875),\n", + " 'pct_low': -0.7801782111071534,\n", + " 'permutation_count': 5000,\n", + " 'permutations': array([ 0.17221029, 0.03112419, -0.13911387, ..., -0.38007941,\n", + " 0.30261507, -0.09073054]),\n", + " 'permutations_var': array([0.07201642, 0.07251104, 0.07219407, ..., 0.07003705, 0.07094885,\n", + " 0.07238581]),\n", + " 'proportional_difference': nan,\n", + " 'pvalue_brunner_munzel': nan,\n", + " 'pvalue_kruskal': nan,\n", + " 'pvalue_mann_whitney': 0.5201446121616038,\n", + " 'pvalue_mcnemar': nan,\n", + " 'pvalue_paired_students_t': nan,\n", + " 'pvalue_permutation': 0.3484,\n", + " 'pvalue_students_t': 0.34743913903372836,\n", + " 'pvalue_welch': 0.3474493875548964,\n", + " 'pvalue_wilcoxon': nan,\n", + " 'random_seed': 12345,\n", + " 'resamples': 5000,\n", + " 'statistic_brunner_munzel': nan,\n", + " 'statistic_kruskal': nan,\n", + " 'statistic_mann_whitney': 494.0,\n", + " 'statistic_mcnemar': nan,\n", + " 'statistic_paired_students_t': nan,\n", + " 'statistic_students_t': 0.9472545159069105,\n", + " 'statistic_welch': 0.9472545159069105,\n", + " 'statistic_wilcoxon': nan}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "effsize.to_dict() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb366b18", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class EffectSizeDataFrame(object):\n", + " \"\"\"A class that generates and stores the results of bootstrapped effect\n", + " sizes for several comparisons.\"\"\"\n", + "\n", + " def __init__(self, dabest, effect_size,\n", + " is_paired, ci=95, proportional=False,\n", + " resamples=5000, \n", + " permutation_count=5000,\n", + " random_seed=12345, \n", + " x1_level=None, x2=None, \n", + " delta2=False, experiment_label=None,\n", + " mini_meta=False):\n", + " \"\"\"\n", + " Parses the data from a Dabest object, enabling plotting and printing\n", + " capability for the effect size of interest.\n", + " \"\"\"\n", + "\n", + " self.__dabest_obj = dabest\n", + " self.__effect_size = effect_size\n", + " self.__is_paired = is_paired\n", + " self.__ci = ci\n", + " self.__resamples = resamples\n", + " self.__permutation_count = permutation_count\n", + " self.__random_seed = random_seed\n", + " self.__proportional = proportional\n", + " self.__x1_level = x1_level\n", + " self.__experiment_label = experiment_label \n", + " self.__x2 = x2\n", + " self.__delta2 = delta2 \n", + " self.__mini_meta = mini_meta\n", + "\n", + "\n", + " def __pre_calc(self):\n", + " import pandas as pd\n", + " from .misc_tools import print_greeting, get_varname\n", + "\n", + " idx = self.__dabest_obj.idx\n", + " dat = self.__dabest_obj._plot_data\n", + " xvar = self.__dabest_obj._xvar\n", + " yvar = self.__dabest_obj._yvar\n", + "\n", + " out = []\n", + " reprs = []\n", + "\n", + " for j, current_tuple in enumerate(idx):\n", + " if self.__is_paired!=\"sequential\":\n", + " cname = current_tuple[0]\n", + " control = dat[dat[xvar] == cname][yvar].copy()\n", + "\n", + " for ix, tname in enumerate(current_tuple[1:]):\n", + " if self.__is_paired == \"sequential\":\n", + " cname = current_tuple[ix]\n", + " control = dat[dat[xvar] == cname][yvar].copy()\n", + " test = dat[dat[xvar] == tname][yvar].copy()\n", + "\n", + " result = TwoGroupsEffectSize(control, test,\n", + " self.__effect_size,\n", + " self.__proportional,\n", + " self.__is_paired,\n", + " self.__ci,\n", + " self.__resamples,\n", + " self.__permutation_count,\n", + " self.__random_seed)\n", + " r_dict = result.to_dict()\n", + " r_dict[\"control\"] = cname\n", + " r_dict[\"test\"] = tname\n", + " r_dict[\"control_N\"] = int(len(control))\n", + " r_dict[\"test_N\"] = int(len(test))\n", + " out.append(r_dict)\n", + " if j == len(idx)-1 and ix == len(current_tuple)-2:\n", + " if self.__delta2 and self.__effect_size == \"mean_diff\":\n", + " resamp_count = False\n", + " def_pval = False\n", + " elif self.__mini_meta and self.__effect_size == \"mean_diff\":\n", + " resamp_count = False\n", + " def_pval = False\n", + " else:\n", + " resamp_count = True\n", + " def_pval = True\n", + " else:\n", + " resamp_count = False\n", + " def_pval = False\n", + "\n", + " text_repr = result.__repr__(show_resample_count=resamp_count,\n", + " define_pval=def_pval)\n", + "\n", + " to_replace = \"between {} and {} is\".format(cname, tname)\n", + " text_repr = text_repr.replace(\"is\", to_replace, 1)\n", + "\n", + " reprs.append(text_repr)\n", + "\n", + "\n", + " self.__for_print = \"\\n\\n\".join(reprs)\n", + "\n", + " out_ = pd.DataFrame(out)\n", + "\n", + " columns_in_order = ['control', 'test', 'control_N', 'test_N',\n", + " 'effect_size', 'is_paired',\n", + " 'difference', 'ci',\n", + "\n", + " 'bca_low', 'bca_high', 'bca_interval_idx',\n", + " 'pct_low', 'pct_high', 'pct_interval_idx',\n", + " \n", + " 'bootstraps', 'resamples', 'random_seed',\n", + " \n", + " 'permutations', 'pvalue_permutation', 'permutation_count', 'permutations_var',\n", + " \n", + " 'pvalue_welch',\n", + " 'statistic_welch',\n", + "\n", + " 'pvalue_students_t',\n", + " 'statistic_students_t',\n", + "\n", + " 'pvalue_mann_whitney',\n", + " 'statistic_mann_whitney',\n", + "\n", + " 'pvalue_brunner_munzel',\n", + " 'statistic_brunner_munzel',\n", + "\n", + " 'pvalue_wilcoxon',\n", + " 'statistic_wilcoxon',\n", + "\n", + " 'pvalue_mcnemar',\n", + " 'statistic_mcnemar',\n", + "\n", + " 'pvalue_paired_students_t',\n", + " 'statistic_paired_students_t',\n", + "\n", + " 'pvalue_kruskal',\n", + " 'statistic_kruskal',\n", + " 'proportional_difference'\n", + " ]\n", + " self.__results = out_.reindex(columns=columns_in_order)\n", + " self.__results.dropna(axis=\"columns\", how=\"all\", inplace=True)\n", + " \n", + " # Add the is_paired column back when is_paired is None\n", + " if self.is_paired is None:\n", + " self.__results.insert(5, 'is_paired', self.__results.apply(lambda _: None, axis=1))\n", + " \n", + " # Create and compute the delta-delta statistics\n", + " if self.__delta2 is True and self.__effect_size == \"mean_diff\":\n", + " self.__delta_delta = DeltaDelta(self,\n", + " self.__permutation_count,\n", + " self.__ci)\n", + " reprs.append(self.__delta_delta.__repr__(header=False))\n", + " elif self.__delta2 is True and self.__effect_size != \"mean_diff\":\n", + " self.__delta_delta = \"Delta-delta is not supported for {}.\".format(self.__effect_size)\n", + " else:\n", + " self.__delta_delta = \"`delta2` is False; delta-delta is therefore not calculated.\"\n", + "\n", + " # Create and compute the weighted average statistics\n", + " if self.__mini_meta is True and self.__effect_size == \"mean_diff\":\n", + " self.__mini_meta_delta = MiniMetaDelta(self,\n", + " self.__permutation_count,\n", + " self.__ci)\n", + " reprs.append(self.__mini_meta_delta.__repr__(header=False))\n", + " elif self.__mini_meta is True and self.__effect_size != \"mean_diff\":\n", + " self.__mini_meta_delta = \"Weighted delta is not supported for {}.\".format(self.__effect_size)\n", + " else:\n", + " self.__mini_meta_delta = \"`mini_meta` is False; weighted delta is therefore not calculated.\"\n", + " \n", + " \n", + " varname = get_varname(self.__dabest_obj)\n", + " lastline = \"To get the results of all valid statistical tests, \" +\\\n", + " \"use `{}.{}.statistical_tests`\".format(varname, self.__effect_size)\n", + " reprs.append(lastline)\n", + "\n", + " reprs.insert(0, print_greeting())\n", + "\n", + " self.__for_print = \"\\n\\n\".join(reprs)\n", + "\n", + "\n", + " def __repr__(self):\n", + " try:\n", + " return self.__for_print\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__for_print\n", + " \n", + " \n", + " \n", + " def __calc_lqrt(self):\n", + " import lqrt\n", + " import pandas as pd\n", + " \n", + " rnd_seed = self.__random_seed\n", + " db_obj = self.__dabest_obj\n", + " dat = db_obj._plot_data\n", + " xvar = db_obj._xvar\n", + " yvar = db_obj._yvar\n", + " delta2 = self.__delta2\n", + " \n", + "\n", + " out = []\n", + "\n", + " for j, current_tuple in enumerate(db_obj.idx):\n", + " if self.__is_paired != \"sequential\":\n", + " cname = current_tuple[0]\n", + " control = dat[dat[xvar] == cname][yvar].copy()\n", + "\n", + " for ix, tname in enumerate(current_tuple[1:]):\n", + " if self.__is_paired == \"sequential\":\n", + " cname = current_tuple[ix]\n", + " control = dat[dat[xvar] == cname][yvar].copy()\n", + " test = dat[dat[xvar] == tname][yvar].copy()\n", + " \n", + " if self.__is_paired: \n", + " # Refactored here in v0.3.0 for performance issues.\n", + " lqrt_result = lqrt.lqrtest_rel(control, test, \n", + " random_state=rnd_seed)\n", + " \n", + " out.append({\"control\": cname, \"test\": tname, \n", + " \"control_N\": int(len(control)), \n", + " \"test_N\": int(len(test)),\n", + " \"pvalue_paired_lqrt\": lqrt_result.pvalue,\n", + " \"statistic_paired_lqrt\": lqrt_result.statistic\n", + " })\n", + "\n", + " else:\n", + " # Likelihood Q-Ratio test:\n", + " lqrt_equal_var_result = lqrt.lqrtest_ind(control, test, \n", + " random_state=rnd_seed,\n", + " equal_var=True)\n", + " \n", + " \n", + " lqrt_unequal_var_result = lqrt.lqrtest_ind(control, test, \n", + " random_state=rnd_seed,\n", + " equal_var=False)\n", + " \n", + " out.append({\"control\": cname, \"test\": tname, \n", + " \"control_N\": int(len(control)), \n", + " \"test_N\": int(len(test)),\n", + " \n", + " \"pvalue_lqrt_equal_var\" : lqrt_equal_var_result.pvalue,\n", + " \"statistic_lqrt_equal_var\" : lqrt_equal_var_result.statistic,\n", + " \"pvalue_lqrt_unequal_var\" : lqrt_unequal_var_result.pvalue,\n", + " \"statistic_lqrt_unequal_var\" : lqrt_unequal_var_result.statistic,\n", + " }) \n", + " self.__lqrt_results = pd.DataFrame(out)\n", + "\n", + "\n", + " def plot(self, color_col=None,\n", + "\n", + " raw_marker_size=6, es_marker_size=9,\n", + "\n", + " swarm_label=None, barchart_label=None, contrast_label=None, delta2_label=None,\n", + " swarm_ylim=None, barchart_ylim=None, contrast_ylim=None, delta2_ylim=None,\n", + "\n", + " custom_palette=None, swarm_desat=0.5, halfviolin_desat=1,\n", + " halfviolin_alpha=0.8, \n", + "\n", + " face_color = None,\n", + " #bar plot\n", + " bar_label=None, bar_desat=0.5, bar_width = 0.5,bar_ylim = None,\n", + " # error bar of proportion plot\n", + " ci=None, ci_type='bca', err_color=None,\n", + "\n", + " float_contrast=True,\n", + " show_pairs=True,\n", + " show_delta2=True,\n", + " show_mini_meta=True,\n", + " group_summaries=None,\n", + " group_summaries_offset=0.1,\n", + "\n", + " fig_size=None,\n", + " dpi=100,\n", + " ax=None,\n", + "\n", + " swarmplot_kwargs=None,\n", + " barplot_kwargs=None,\n", + " violinplot_kwargs=None,\n", + " slopegraph_kwargs=None,\n", + " sankey_kwargs=None,\n", + " reflines_kwargs=None,\n", + " group_summary_kwargs=None,\n", + " legend_kwargs=None):\n", + "\n", + " \"\"\"\n", + " Creates an estimation plot for the effect size of interest.\n", + " \n", + "\n", + " Parameters\n", + " ----------\n", + " color_col : string, default None\n", + " Column to be used for colors.\n", + " raw_marker_size : float, default 6\n", + " The diameter (in points) of the marker dots plotted in the\n", + " swarmplot.\n", + " es_marker_size : float, default 9\n", + " The size (in points) of the effect size points on the difference\n", + " axes.\n", + " swarm_label, contrast_label, delta2_label : strings, default None\n", + " Set labels for the y-axis of the swarmplot and the contrast plot,\n", + " respectively. If `swarm_label` is not specified, it defaults to\n", + " \"value\", unless a column name was passed to `y`. If\n", + " `contrast_label` is not specified, it defaults to the effect size\n", + " being plotted. If `delta2_label` is not specifed, it defaults to \n", + " \"delta - delta\"\n", + " swarm_ylim, contrast_ylim, delta2_ylim : tuples, default None\n", + " The desired y-limits of the raw data (swarmplot) axes, the\n", + " difference axes and the delta-delta axes respectively, as a tuple. \n", + " These will be autoscaled to sensible values if they are not \n", + " specified. The delta2 axes and contrast axes should have the same \n", + " limits for y. When `show_delta2` is True, if both of the `contrast_ylim`\n", + " and `delta2_ylim` are not None, then they must be specified with the \n", + " same values; when `show_delta2` is True and only one of them is specified,\n", + " then the other will automatically be assigned with the same value.\n", + " Specifying `delta2_ylim` does not have any effect when `show_delta2` is\n", + " False. \n", + " custom_palette : dict, list, or matplotlib color palette, default None\n", + " This keyword accepts a dictionary with {'group':'color'} pairings,\n", + " a list of RGB colors, or a specified matplotlib palette. This\n", + " palette will be used to color the swarmplot. If `color_col` is not\n", + " specified, then each group will be colored in sequence according\n", + " to the default palette currently used by matplotlib.\n", + " Please take a look at the seaborn commands `color_palette`\n", + " and `cubehelix_palette` to generate a custom palette. Both\n", + " these functions generate a list of RGB colors.\n", + " See:\n", + " https://seaborn.pydata.org/generated/seaborn.color_palette.html\n", + " https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html\n", + " The named colors of matplotlib can be found here:\n", + " https://matplotlib.org/examples/color/named_colors.html\n", + " swarm_desat : float, default 1\n", + " Decreases the saturation of the colors in the swarmplot by the\n", + " desired proportion. Uses `seaborn.desaturate()` to acheive this.\n", + " halfviolin_desat : float, default 0.5\n", + " Decreases the saturation of the colors of the half-violin bootstrap\n", + " curves by the desired proportion. Uses `seaborn.desaturate()` to\n", + " acheive this.\n", + " halfviolin_alpha : float, default 0.8\n", + " The alpha (transparency) level of the half-violin bootstrap curves. \n", + " float_contrast : boolean, default True\n", + " Whether or not to display the halfviolin bootstrapped difference\n", + " distribution alongside the raw data.\n", + " show_pairs : boolean, default True\n", + " If the data is paired, whether or not to show the raw data as a\n", + " swarmplot, or as slopegraph, with a line joining each pair of\n", + " observations.\n", + " show_delta2, show_mini_meta : boolean, default True\n", + " If delta-delta or mini-meta delta is calculated, whether or not to \n", + " show the delta-delta plot or mini-meta plot.\n", + " group_summaries : ['mean_sd', 'median_quartiles', 'None'], default None.\n", + " Plots the summary statistics for each group. If 'mean_sd', then\n", + " the mean and standard deviation of each group is plotted as a\n", + " notched line beside each group. If 'median_quantiles', then the\n", + " median and 25th and 75th percentiles of each group is plotted\n", + " instead. If 'None', the summaries are not shown.\n", + " group_summaries_offset : float, default 0.1\n", + " If group summaries are displayed, they will be offset from the raw\n", + " data swarmplot groups by this value. \n", + " fig_size : tuple, default None\n", + " The desired dimensions of the figure as a (length, width) tuple.\n", + " dpi : int, default 100\n", + " The dots per inch of the resulting figure.\n", + " ax : matplotlib.Axes, default None\n", + " Provide an existing Axes for the plots to be created. If no Axes is\n", + " specified, a new matplotlib Figure will be created.\n", + " swarmplot_kwargs : dict, default None\n", + " Pass any keyword arguments accepted by the seaborn `swarmplot`\n", + " command here, as a dict. If None, the following keywords are\n", + " passed to sns.swarmplot : {'size':`raw_marker_size`}.\n", + " violinplot_kwargs : dict, default None\n", + " Pass any keyword arguments accepted by the matplotlib `\n", + " pyplot.violinplot` command here, as a dict. If None, the following\n", + " keywords are passed to violinplot : {'widths':0.5, 'vert':True,\n", + " 'showextrema':False, 'showmedians':False}.\n", + " slopegraph_kwargs : dict, default None\n", + " This will change the appearance of the lines used to join each pair\n", + " of observations when `show_pairs=True`. Pass any keyword arguments\n", + " accepted by matplotlib `plot()` function here, as a dict.\n", + " If None, the following keywords are\n", + " passed to plot() : {'linewidth':1, 'alpha':0.5}.\n", + " sankey_kwargs: dict, default None\n", + " Whis will change the appearance of the sankey diagram used to depict\n", + " paired proportional data when `show_pairs=True` and `proportional=True`. \n", + " Pass any keyword arguments accepted by plot_tools.sankeydiag() function\n", + " here, as a dict. If None, the following keywords are passed to sankey diagram:\n", + " {\"width\": 0.5, \"align\": \"center\", \"alpha\": 0.4, \"bar_width\": 0.1, \"rightColor\": False}\n", + " reflines_kwargs : dict, default None\n", + " This will change the appearance of the zero reference lines. Pass\n", + " any keyword arguments accepted by the matplotlib Axes `hlines`\n", + " command here, as a dict. If None, the following keywords are\n", + " passed to Axes.hlines : {'linestyle':'solid', 'linewidth':0.75,\n", + " 'zorder':2, 'color' : default y-tick color}.\n", + " group_summary_kwargs : dict, default None\n", + " Pass any keyword arguments accepted by the matplotlib.lines.Line2D\n", + " command here, as a dict. This will change the appearance of the\n", + " vertical summary lines for each group, if `group_summaries` is not\n", + " 'None'. If None, the following keywords are passed to\n", + " matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3}.\n", + " legend_kwargs : dict, default None\n", + " Pass any keyword arguments accepted by the matplotlib Axes\n", + " `legend` command here, as a dict. If None, the following keywords\n", + " are passed to matplotlib.Axes.legend : {'loc':'upper left',\n", + " 'frameon':False}.\n", + "\n", + "\n", + " Returns\n", + " -------\n", + " A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``.\n", + " \n", + " The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it.\n", + " \n", + " If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax`` \n", + " itself, while the effect size axes is accessed at ``ax.contrast_axes``.\n", + " See the last example below.\n", + " \n", + "\n", + "\n", + " \"\"\"\n", + "\n", + " from .plotter import EffectSizeDataFramePlotter\n", + "\n", + " if hasattr(self, \"results\") is False:\n", + " self.__pre_calc()\n", + "\n", + " if self.__delta2:\n", + " color_col = self.__x2\n", + "\n", + " # if self.__proportional:\n", + " # raw_marker_size = 0.01\n", + " \n", + " all_kwargs = locals()\n", + " del all_kwargs[\"self\"]\n", + "\n", + " out = EffectSizeDataFramePlotter(self, **all_kwargs)\n", + "\n", + " return out\n", + "\n", + "\n", + " @property\n", + " def proportional(self):\n", + " \"\"\"\n", + " Returns the proportional parameter\n", + " class.\n", + " \"\"\"\n", + " return self.__proportional\n", + "\n", + " @property\n", + " def results(self):\n", + " \"\"\"Prints all pairwise comparisons nicely.\"\"\"\n", + " try:\n", + " return self.__results\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__results\n", + "\n", + "\n", + "\n", + " @property\n", + " def statistical_tests(self):\n", + " results_df = self.results\n", + "\n", + " # Select only the statistics and p-values.\n", + " stats_columns = [c for c in results_df.columns\n", + " if c.startswith(\"statistic\") or c.startswith(\"pvalue\")]\n", + "\n", + " default_cols = ['control', 'test', 'control_N', 'test_N',\n", + " 'effect_size', 'is_paired',\n", + " 'difference', 'ci', 'bca_low', 'bca_high']\n", + "\n", + " cols_of_interest = default_cols + stats_columns\n", + "\n", + " return results_df[cols_of_interest]\n", + "\n", + "\n", + " @property\n", + " def _for_print(self):\n", + " return self.__for_print\n", + "\n", + " @property\n", + " def _plot_data(self):\n", + " return self.__dabest_obj._plot_data\n", + "\n", + " @property\n", + " def idx(self):\n", + " return self.__dabest_obj.idx\n", + "\n", + " @property\n", + " def xvar(self):\n", + " return self.__dabest_obj._xvar\n", + "\n", + " @property\n", + " def yvar(self):\n", + " return self.__dabest_obj._yvar\n", + "\n", + " @property\n", + " def is_paired(self):\n", + " return self.__is_paired\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " The width of the confidence interval being produced, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + " @property\n", + " def x1_level(self):\n", + " return self.__x1_level\n", + "\n", + "\n", + " @property\n", + " def x2(self):\n", + " return self.__x2\n", + "\n", + "\n", + " @property\n", + " def experiment_label(self):\n", + " return self.__experiment_label\n", + " \n", + "\n", + " @property\n", + " def delta2(self):\n", + " return self.__delta2\n", + " \n", + "\n", + " @property\n", + " def resamples(self):\n", + " \"\"\"\n", + " The number of resamples (with replacement) during bootstrap resampling.\"\n", + " \"\"\"\n", + " return self.__resamples\n", + "\n", + " @property\n", + " def random_seed(self):\n", + " \"\"\"\n", + " The seed used by `numpy.seed()` for bootstrap resampling.\n", + " \"\"\"\n", + " return self.__random_seed\n", + "\n", + " @property\n", + " def effect_size(self):\n", + " \"\"\"The type of effect size being computed.\"\"\"\n", + " return self.__effect_size\n", + "\n", + " @property\n", + " def dabest_obj(self):\n", + " \"\"\"\n", + " Returns the `dabest` object that invoked the current EffectSizeDataFrame\n", + " class.\n", + " \"\"\"\n", + " return self.__dabest_obj\n", + "\n", + " @property\n", + " def proportional(self):\n", + " \"\"\"\n", + " Returns the proportional parameter\n", + " class.\n", + " \"\"\"\n", + " return self.__proportional\n", + " \n", + " @property\n", + " def lqrt(self):\n", + " \"\"\"Returns all pairwise Lq-Likelihood Ratio Type test results \n", + " as a pandas DataFrame.\n", + " \n", + " For more information on LqRT tests, see https://arxiv.org/abs/1911.11922\n", + " \"\"\"\n", + " try:\n", + " return self.__lqrt_results\n", + " except AttributeError:\n", + " self.__calc_lqrt()\n", + " return self.__lqrt_results\n", + " \n", + " \n", + " @property\n", + " def mini_meta(self):\n", + " \"\"\"\n", + " Returns the mini_meta boolean parameter.\n", + " \"\"\"\n", + " return self.__mini_meta\n", + "\n", + " \n", + " @property\n", + " def mini_meta_delta(self):\n", + " \"\"\"\n", + " Returns the mini_meta results.\n", + " \"\"\"\n", + " try:\n", + " return self.__mini_meta_delta\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__mini_meta_delta\n", + "\n", + " \n", + " @property\n", + " def delta_delta(self):\n", + " \"\"\"\n", + " Returns the mini_meta results.\n", + " \"\"\"\n", + " try:\n", + " return self.__delta_delta\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__delta_delta\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e1b8353", + "metadata": {}, + "source": [ + "#### Example: plot\n", + "\n", + "Create a Gardner-Altman estimation plot for the mean difference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a151b86", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "# pop_size = 10000 # Size of each population.\n", + "Ns = 20 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns/2).tolist()\n", + "males = np.repeat('Male', Ns/2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns+1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3,\n", + " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", + " 'Gender' : gender, 'ID' : id_col\n", + " })\n", + "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91d15864", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig1 = my_data.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "a37d4519", + "metadata": {}, + "source": [ + " Create a Gardner-Altman plot for the Hedges' g effect size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e9cac0b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig2 = my_data.hedges_g.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "f40f8fe0", + "metadata": {}, + "source": [ + "Create a Cumming estimation plot for the mean difference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0e6a68e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig3 = my_data.mean_diff.plot(float_contrast=True);" + ] + }, + { + "cell_type": "markdown", + "id": "1ee59074", + "metadata": {}, + "source": [ + " Create a paired Gardner-Altman plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89a19ee0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_data_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", + " id_col = \"ID\", paired='baseline')\n", + "fig4 = my_data_paired.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "3c37066a", + "metadata": {}, + "source": [ + "Create a multi-group Cumming plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "896cac2a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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t9g+j05czUVharnX/ExfTUV1TC5mVST/5IjIZEcERWDNvDXam7ERGfgbcHdyRWZiJDUc2qPc5fe00tiVtw/yx8zGy50jEn47HV9u/0vgclaDCrpRdkFvJMW/MvBbH88kTn7T4WKLm0vujkG3btuHdd9+Fn5+fxvaOHTvi6tWr+j49/YNPnogMy8HGARP7TsTc0XPRybcTtp/cXmcflaDCp5s/RaGiEL8f/l3rZ+04uQPFZcVa24mMid5/hS0rK4Otbd2VGPPy8jSKV1HLdfL3gouDrdZei4iOAZDL2FtBJJZtJ7dpbatV1WLHyR1IzUrVuk+NsgZp2WmI7BCpj/CohWrKynAlPh7FV69C7uSEoCFD4ODrK3ZYotN7j8XgwYPx3Xffqd9LJBKoVCq89957GDp0qL5P3yZYWUoxY1S/etsspRZa24jIMAobmXpbWFYIuVXDv2jZWdu1+Pzzv56PGR/PwPyv57f4M0hT3rlz+HvuXCR98w0u79yJc+vXI+6553Buw4bGDzZzev819r333sOQIUNw/PhxVFdXY9GiRTh9+jQKCgpw4MABfZ++zRg/sAesLC3x0/aj6pkhnfy98OT4QegWzOltRGLyd/dH8tVkre2BHoEY0nUItiZtrbfd19UXnXxbvtBgoaIQ+aX5LT6eNCmrq3HwvfdQc/eK2YKAlB9/hFunTvDo2lWc4IyA3hOLsLAwJCcn44svvoBUKkVZWRkmTZqEuXPnwsfHR9+nb1Ni+nXFqL5hyMovhqVUWmdaKhGJY2yvsdiSuAUqQVWnzdHWEdHdotGrQy8kXkpEbkmuRruV1ApzRs2BRCIxVLjUiOuHD6OqWPuYl7StW5lY6Ju3t7dZLl9ujCQSCXzdncUOw6xUFedCgABrp4Yrn5qjG8k7kZ3wNyoKMiFzcId3xCj49BoDiYX5r83TmNPXTt8cVFlejECPQIyOHA3Pev6O5BTnQG4pxwvjX8Anf3+CGmWNus3J1gmxU2NhbWUNaytrfPj4h9hwZAP2n9mP6tpqdA/qjgeiHlBPSS2rLMOWE1tw6PwhKFVKRAZHYlyvcXB1cDXY9yZAkZ3dYHtpI+3mTu+Jxd69extsHzx4sL5DIGqRgotHcXXP9yi7cQkAYOsRiIDoaXDvPFBjP2V1BSryM2Bp4wBrZy8xQm0RRdZFXD+0HsVXk2FhKYN7l4Fo138SZPY3/5FKi/sCWcc3qfevrSjFpa1fovhqMjpPXgyJpO2uCPDV9q80po4evnAYG45swKsPvIo+IX0AACcuncDq3avVgzJ9XHzw5PAnoRSU6joWg8MGa4ytcLV3xRP3PoEn7n2izjkLFYVY9N0iZBTcLnh3IfMC4k7E4Z0Z7yDAPUBfX5fuYuvm1nC7u7uBIjFOek8shgwZUmfbnV16SmXdFQCJxFaQegxn1r0J3NF1XZ57Fed+W4HOk1+Ge5dBUClrcXX3GmQnxkH5TyEkR78wBMc8Dfs7ih61VLWiANcPrUfemX1Q1VbDKbAb/KImw6Fd6xewKkxLwJl1b0C4Y7GsjMMbkHdmP7rPeh815cUaScWd8s8dRGHqcbh27NvqOEzRsdRjGknFLdW11Vi5YSW+e+47pGalInZtLGpVt///ZhVm4YutX2D+2Pl4/N7Hm33e1btWayQVtxSVFeGzzZ/h3UffbfZnUsv4DxiAk99+i5ry+mfiBQ8fbuCIjIvef+UoLCzUeOXk5CAuLg59+vTBtm3ap2ARienqnu81korbBFzd8z0EQUDqpk+QcXiDOqkAgJLrZ5Dyw2JUFt7uChUEFUqunUFB6vEmL8xVVZqPk6tfROaRDaguzUNtRQnyzx1E8rf/h/wLR5r8PRRZF5F7ei9Krp25Ix4BaXFfaCQV6vOW5CJ930/IPR3f4Ofmnmq43ZzFnYjT2lZWVYZ9Z/bhh/gfNJKKO/2498d6l1QHbvZyvPLDK5iwYgIefP9B/Gfzf5BbnIvq2mrsPaO99zclPQXZhW27+92QLG1s0P+FFyCVyeq0dRo/Hj6RbXtasN57LJycnOpsGzFiBORyOV544QUkJCToOwSiZqkqzUdZdprW9or86yi6fAI5KbvqbVdWliHj6J/oMOpfKEw7jrQtX6Cy6OYPfYmFJTy7D0OHmGdgYVn3h9It1/atRVVxTp3tgqoWl+K+gGvHPg0+iqgszMK5DSuhyLyg3mbrHoBO978EVU01KguztB6beyoenuHDtLYDQG219kqv5u7uwZV3yyrMQkp6itb2/NJ8pGalIrRdqMb23ad244M/P1AP8FQoFdiSuAVHLx7F0qlLUV1b3eB5i8qL4O3i3cRvQa3lHRGBmE8+waXt22/XsRg6FO6dW9+jaOpEq5rk4eGB8+fPi3V6Iu2aUKa0JP00AO37FaUlQJF1EWfWvanRMyCoanEjaRtUtdUInfiS1uNzT+/R2lZVkouS9DNwCuxWb7uqtgYpP76KqrsWsCrPS8fpH19DcMwcrZ8NAKqaSji064TsxM1a93HUweMYU+Xr4ttgMSsfl+bPdqtR1uB/2/9X76yR/NJ8bE7YDDcHN61TRmWWMq6aKgJbd3d0e/hhscMwOnp/FJKcnKzxOnnyJOLi4vDMM8+gR48e+j49UbPJHd1h59Vea7u1qy+s7BsevAULC1w/tL7exw0AkHt6r8bjkjsJggBldWWDH69soMcg7+y+OknFLTXlxSjPvQKJVPvvFPY+HeHRNRpyLbNgLG0c4BUxqsH4zNnYXmO1tjnbOWNItyEIDwjXuo+bgxtCfEI0tqVcTUFhmfbHZPvO7sP43uO1tt8bfi8cbDi9nIyD3hOLnj17IiIiAj179lT/ecyYMaiursbXX3+t79MTtUhA9AxAy6OGwOjpcOvUV2s7ALh16o/iBgoiQVChWEt3uUQigcNd3eQa7RaWsPcJgaBSIu/cAVze+Q2u7f9FnajcOZ6iPmU3LsOru/bBZX5Rk2FhKUO3aW/BzitYo83axRddH34TMjvnBs9hzsIDw/HYsMcggWZdiVurmsosZZgePR2WFvUnb5P6TcLGYxvxvx3/Q1xiHCqqK1BVU9XgOStrKvHAgAcwqmfdhK5PSB/MHjm75V+ISMf0/ijk8uXLGu8tLCzg4eEBa2trfZ/a7FTX1GLvyYvIzCuCl6sjBvfoBBt5y5dSJu3cOvVDlwdewdU936M89+ZieTZufggYPA0eXW9OkfbtMx6ZR/+sc6zMwQ2+fScg93TDU60tLLWXcPYb8ADOrnuz3jbP7vdCVVuDxP/OQUX+dfX2q/E/wH/QQ5DKGr63pDJrBI96GqraGuSc2q0epCqV2SAgejrcw+65+X1dfREx+z8ouX4WFQWZkDu6wymwe6OFmmT/LM0tM+MluqcMmIIBnQeo61i092yPYeHD1GW3wwPDseyhZVizew0uZl0EcLN6Zs+gnlize41GLYtvdn2DF8a/AEsLS60DPrsFdIOFxALPjXsOk6Mma9SxaE1FTmqdqtJSXNm1C8Xp6eq1QpwCOO1XIghtZ93LxMRE9OrVCwkJCYg0sVG7py5lYNnqTShS3J6BYG8jx6uPjkHvzoEiRmb+KgqyAAiwdvHR+EdVEARkHNmArKMbUVWSC4mFJdxC+yNo2GOwdvHG5Z3fIONQ/StWSmU26PHYh8g9Ew9F5gVYWtvDo9sQuIT0UZ8jK2EzruxaA2VV2c2DJBbw6DYEHcfOQ/K3L0Gh5Tl/4LDHcHXXaq3fJ2xqrHqqaGXRDRRfTYGFlRwuHXrBUl53wUBqndziXNSqalFTW4O5X82td0aIvbU9okKj6l0B1UJigTcffhMRwREtjmHGxzOQX5oPNwc3fP/c9y3+HLot98wZ7H/nHdTeNeW069SpCJsyRaSojINeeiw++eSTJu87fz4XxWlMSVklXvvqT5RVao4KV1RUYdnqv7D6lVlwd7LX2F8qlcDOmqvH6oKNa/2D8SQSCfz6T0K7fhNRU1YEqcwGUpmNur1dv/uRd2Y/qorrjnfw7DECJ1e/oDFVNfd0PNzDBiP0/pcgkVjAp9cYeIYPQ+GlBKhqquDo3w3Wzp4ouX5Wa1IBAEVpx+HVcxRu1LPuhGunfnAJ6a1+b+3sZVJFvUxFaUUpCssK4eHoAQ8nDwDAf7f9V+s0U0WlAh28O8BKaoVtSdvUPRcu9i54cviTrUoqSPeUVVU4+P77dZIKADj9yy9w79wZnuHax9mYO70kFh9++GGT9pNIJEwsmmD7sTN1kopbKqtrseXQKcyI6Y/9yan4cdsRpGbkQiK5uVz642MHIDTA+KagzfngJxSWlsPFwRafv/iI2OG0ikRioa5WeSeZvQu6z3oP1/atRe6p3VBWV8LetyPa9Z+Ey9u/0kgqbsk7sxfO7XvC+5/Bkaraaigry6GqrVIP2CzPTW8wnvK8a+g2fQUcfDsiK2Gz+jGGd0QMfPtOaNMVM/WtoLQAX277Uv2oQm4lx73h9+LJ4U/iWt61Bo/NLMjEs2OexbTB03Au4xzkVnJ0D+wOywYG2pI4rh8+jOqSEq3taVu3MrHQtbvHVVDrXMrKa7R9x/GzePfH27+hCgKQeCEdZ65k4oNnp6CTv3H9VlpYWo68YkXjO5o4uYMbQsbMRciYuRAEFSQSCxRcPIrqBlaavJG0Dd4Ro3D94K9I3/sTVHfUL3Dt1A+e4fc2eE6ZvQskEgm8I0fDO3K0zr5LUyV9/RyqFYWQ2bug5xMfG/z8YqmorsDLP7yM63eMe6mqqcLmxM24nn+93nVE7uT6T3LqYu+CqNAovcZKrdPYWiGKnLo1aNoS/upiAlzsG37u7WRng2/+rn8J+srqWnwXd1gfYVEz3eopqCppOFGsKslD7um9uLJrjUZSAQAFF44g7+x+yBy0r0Xg1XNk64NthWpFIapL85tcZdRc7ErZpZFU3Cn5ajLaNzCFWWohxb3dG04YyXjYeTX8i5qdh4eBIjFOBulju379OjZu3Ij09HRUV2v+oPz3v/9tiBBM2oi+Yfhl13Gt7Z0DvfD3Ie2V/o6dvYLq2lrILNmlagxsGilkZOPWDhn1rEVxS97Z/Qi9fxEu/vURVDWa9S5cQvrAO3KMTuKk5jmWeqzB9tySXDwQ9QB+O/SbxnYLiQXmjp4LN4dGaqOQ0fCLikLSmjWoUdTf6xo8YoSBIzIuev+XZufOnZgwYQLat2+P8+fPo1u3brhy5QoEQTC5mRliCfRyxawxA7Bm88E6bQ8O6wU/j4an9akEASpVm5n8Y/ScgrrDxt0fFVqeufv0Gotzv6/Q/gGCClIrOSL/9TmyEv5GacZ5WFrbwaPrELh3GcglzUVyd12L+tofv/dxdAvohrgTcVBUKuDv7o8xkWPUy6KTabCUyxG1YAEOrFwJZaVmct/5/vvh3bOnOIEZCb0nFosXL8aLL76IN954Aw4ODvj999/h6emJadOmISYmRt+nNxvTRvRF1yAfbDqYjMy8Yni5OmJMVDf06RyE6ppaONpZo6Ss/mqN4cHtYC1jvQtjIZFI0OWBV3H6p9dRdde6E34DpsC9y0BY2tijtqJU62dYWtvD2tkL7RtZJVNVW42C1GOorVDAoV0o7DyDdPEVqB59QvrgyEXtC8R18euC9/54D/vO7kOtshb21vYI9Q3lcucmyqt7d4z+z39weefO22uFDBkC15CQxg82c3pPLM6ePYuff/755sksLVFRUQF7e3u88cYbuO+++/DMM8/oOwSTcT2nEHFHTqOgpAwBXq4Y1a8rXBxuj6/o2dEfPTv61zlOZmWJh+7tg1Ub99Vps7CQYNrItrm8tTGzdfdHrzmrkHt6LwouHgEggVfPkXD9ZyqoR7ehyDq2sd5jrV18YN8uFJnHNiIrYTMqC7Ng7eQFr8gYtOt7n7rHIvd0PNLivtBIUJyDIxF6/yJYsfyzzg3rPgwbj21Eel7dWTs9gnrguz3faYzBUFQq8Pvh33E9/zpip8YaMlTSERsXF4Q98IDYYRgdvScWdnZ2qKq6Wa7W19cXaWlp6Nq1KwAgL6/hQWxtyfr4RHz5516N9a9+3H4ESx4bhz6dgwDcLMh07NwV7E9OQ61SichOARjcsyNklpaYMrQXLCQSrN15HEWKm9MS/TxcMHv8IPQKZQEtY1SWcwXXD/2mfiSSf+4AnIMj0WnCAgTc8xCKLiVqVNYEAAtLGUJGz8XFP/+tsVBZRUEGruz4GiXXzqDLA6+iNOMczv/xfp2l34suJeLc7ysQPv1tvX+/tsbayhrvzHgHX23/CvvP7keNsgY2MhuM6DECXk5e+GrHV/Ued+TiEZy9fhZd/LoYOGJqqrKcHKTGxSH/3DlYWlvDf9AgBN5zDyys2BNcH70nFv3798eBAwcQFhaGsWPH4sUXX0RKSgrWr1+P/v376/v0JuF8eja++KNu+efK6losX7MZP8U+AStLKZZ8vREJ52//NrT92Fn8vOMYVj4zGW5Odpg8JBITBvXApcw8yCylCPJxq1N+OaewFPFJF1BRVY2wIF/0Cg1otEQz6V5VSR5O/fQalJVlGtuLLiXi9NpY9HziY/SY9T4yj/2FvLP7bxbICgxHu34TUVtZpnX104Lzh1B0KRE3krbVSSpuKb5yEorsNNjzub7OOds546WJL2Hu6LkoLiuGi4MLrK2s8dpPrzV43OELh5lYGKm8s2ex7623UHvHWIobycm4smcPBr/6KqRyFiK8m94Ti3//+99Q/DNydunSpVAoFPjll18QEhLS5EJaAPDFF1/giy++wJUrVwAAXbt2xZIlSzB6tOHn6evapoPaZ3SUV1VjZ8I55BUrNJKKW9JvFOCjdTvw5uz7AABWllKEBtQ/Fer7rYfxw7YjGgM5O/p5Yvns++DqaNfKb0HNkZXwd52k4pay7DQUpiXANaQ3AgY/goDBmgXE0rZ+2eBn557ei9KM8w3uU5pxnomFHtnKbWF7R3n0NrRyglkRVCoc/c9/NJKKW/LOnMGFTZvQZfJkESIzbnqvY/Hmm28iNzcXgiDA1tYWn3/+OZKTk7F+/XoEBja9i97Pzw/vvPMOjh8/juPHj2PYsGG47777cPr0aT1GbxiZeUUNtmfkFWHL4VNa24+evYKcQu0D/QAgPukCvos7XGd2yMXrOXj7+y1NjpV0o7EVSEvSb/69rirORcaRP3DtwDr1MapGllRX1VRC2siaH1wTxLD6hPRpVTuJI/fMGZQ1UOzqyu7dBozmph2LFmHTU09hx6JFBj93U+m9xyI/Px9jx46Fm5sbHnroIcyYMQM9WzAVZ/z48Rrv33rrLXzxxRc4fPiwesyGqfJydQTSMrS2uzrYaiw+djeVIOBGQQkqqmvw2+4EnLiQDktLKQaFh2DykAi4ONhhw94krcefTL2OS5m5CPZt20VdDElq1XD3qYWVHFd2f4vrB3/TeKThFBgOt84DgXoWq7rFMaAr7Lw74Orub+s/t8wGrp36tSxwapERPUZg47GNyC6qW7Exon0EugV0EyEqakxlUVGr2vWhsqgIFQUFBj9vc+i9x2Ljxo3Izs5GbGwsEhIS0KtXL4SFheHtt99WP9ZoLqVSibVr16KsrAxRUdpL31ZVVaGkpET9UmgpZiK2cQO015S3llliVL+ucLTTvhS2RAIUKcrx7L9/RtyR07hRWIqM3CL8sus4nv1wLXKLSnGlkbLgl7O0l5gm3XPvck8DrRJILKS4fmBdnXESxVdTUHT5BORO9T/ukjm4wbP7cPj2GQ+7eh91SNB+5FMai6WR/tlZ22HloysRFRoFi38qsFpbWWNsr7F4/cHXRY6OtHHyrzsL706OAQEounwZhz/8EH8+9hj+evJJJKxa1WAvR1tgkFKMzs7OeOqpp/DUU0/h+vXr+Pnnn/HNN99gyZIlqK2tbfLnpKSkICoqCpWVlbC3t8eGDRsQFhamdf8VK1Zg2bJluvgKehUW5IvHxgzA6rsKYFlZSrF4xmg429tidL9uWqtv9ukchO+3HkFldU2dtpzCUqzZcgjODrZaFzIDGi8bTrrl0W0IbpzcjpJrdR/l+fadgLwz8VqPLbh4DN2mv42ru1ZrjKWw8+6A0IkL1Y85wme8g6xjG5GTshu1lQrY+3ZCu34T4RzUQ/dfiBrl7uiO16e8juKyYhSVF8HD0UNjHAYZH6fAQHh07YpcLY/cvcLDsfPVV6G6o6L0pW3bkHH4MIa+9RYcfOpfGdncGbTGc01NDY4fP44jR47gypUr8Gqk3vrdQkNDkZSUhKKiIvz++++YOXMm4uPjtSYXixcvxoIFC9Tvk5KSEB0d3arvoEs3CkpQq1TB190Jj4zoi35h7RF35DTy/6ljMaZ/N3i63Kw3MGNUf5y/dgNJFzWrNfp5uGBSdARe/lJ7Ceg9J87jkeF9sWbLoXrbPV0c6q2P0VSXs/Jw8NQlqFQq9O4ciC6BbfNmag4LSyt0feQNXD/4O3JObke1ogC2HgHw6T0eXj1H4sBb47UfLKigqq5Aj8f+DUV2GioLsyF38oSDb0eN3SzltvAf9BD8Bz2k529DzeFk5wQnOyexw6Am6vf889j/1lsourOH3cICnSdORObx4xpJxS1VJSU49dNPiHrxRcMFakQMkljs3r0bP/30E37//XcolUpMmjQJf/31F4YNG9asz5HJZAj5p6pZ7969cezYMXz88cf473//W+/+crkc8jumAtnb27f8S+jQsXNX8M2mA0jNuFl10dfdCdNH9seIPl0wd9KQeo+Ryyzx7tOTcOTMZexPSUVtrRKRoQEYGhGKs1cbXmmvukaJsQPCkXAhHSl3jeWwllnhpYdHwsKi/imnOYUl2H7sLIoUFWjv44ahkZ1hI785d1upVOG9n7dhZ8I59f7fxR1G3y5BeH3W2Aarfd4q/HVnAbC2RmpljcDoaQiMnlanzcrWETXlxVqPtbRxBADYe3fg7A4iHaitqkJ1SQnkTk6QymTq7TYuLhj+3nu4kZSEvFt1LAYMQG11Nc6tX6/18zKOHYOyulrjs9oKvScWfn5+yM/Px6hRo/Df//4X48ePh7W19vECzSEIgrr4lqk4cfEaXv9qI5Sq28/OM/OKsfKnrVCqVIjpd3sgqiAIqKyugdzKChYWElhYSBDVLRhR3YI1PrO9jztkVlJU1yjrPWeAlyuc7W3xztP3Y+fxc9ideB7lVdXoGuSL++7pAV9353qP+2NvEr74M15jJsnqzQexfPZ9CA3wxk/bj2okFbccPXsF//1zL56bon21xs9ffERrGwGePYYj49Dv9bbZuPnB0a+zgSMiMk/VZWVI+eEHXN27F8qqKlja2iIoOhrh06bB8p9/qyQSCbwjIuAdEaE+Lv/ChQY/V6itZWKhL0uWLMGUKVPg4tLwQlmNeeWVVzB69Gj4+/ujtLQUa9euxZ49exAXF6ejSA3juy2HNJIKjba4QxjRpwtUKgE/7ziKvw+moKC0HI521ojp1xUzRvWvtxfA0c4ao/p2xV8Hkuv93ClDewEAZJaWGN2/G0b3vzkCvbqmFrtPnMdP249CZmWJ6J6d0CPEDwBw5komPv9jD+6efl+kqMCS/23Emldn4a+D9Z8PuFm868lxg2Bnw+IxTVGem47y/OuQO7jBoV0o/AdORdGlEyi7cUljP6nMBh1GP4vsE1uRe2o3aqvK4diuM3z6jIete8sfZxG1RaqaGux94w0UpqWpt9WWlyN1yxYUX72K6KVLIbGwgLKmBtcOHEDG0aNQ1dbCKzwcfgMGwNLGBrUV9c/Yc/Dzg8xIeskNTe+JxVNPPaWTz7lx4wZmzJiBrKwsODk5oXv37oiLi8MIE1qetqyiCqcuZ2ptzy1SIC0jFz9sO4JDp27/g1JSVol1uxJw9mo23ntmMqTSupN5np44GOWV1diVeE6dDFhZSvHw8D4avSC3ZBcUY9Hn65GVf7u7/a8DyRjcoyNemTEaG/cn10kqbikoLcf2Y2dQWFqu9btU1dQiM68YHf09te5DQFVpPi78+QGKr5xUb7P1DEKnCQvQfeZKZCdtQ96ZfVDVVMEpMBzekTFI2/I5iq/eLqpWlp2GGye3o8uU1+HSgSsGG7MD5w5gW9I2FCgK4Ofmh3G9x6Grv2lPlzdl1w8f1kgq7pR75gyyk5Lg3qUL9r7xBgouXlS3ZScm4sKmTQgYOBCXduyo9/jQCRP0ErMpMOjgzdb4+uuvxQ7BIC5cu6GRVNwpJS0DB0+l4Z4eHeu0ySwt8fL0GDwa0x8nLlyDpaUF+nZuj0tZudh69DT8PV0QFuSr3v/dH7ZqJBW37D15EZ0DvXEtp7DBOHOLFLCylKKmtv7HLxIJ4OygfUrjnA9+QmFpOVwcbNvsYxFBpcTpn15Hee5Vje3lOVdw6qfXEPmvL9Cu731o1/c+ddv1Q79rJBW3qGqrcfGvD9Fn/hrRl02X2bto/Jdu+mjTR9iWtE39Pi07DXtP78XTo57G+D4NDNYlvck8Xv9MuzvbbyQnayQVt1Tk56MkMxMdYmJwaccOCP/McJRaWyNs8mS0HzYMiqwsXDt4ELWVlXDv0gXeERFtYgkFk0kszIGdjRzd2vtq7bXwcHbA5cyG603sS05VJxZllVWoqKqBq4OdevClr7szfN2dceZKJuZ/vBbZBSXqY0PaeeD1WWNRXaNssOdk08EUBHm74sI1rbvA29UJ0T07Ycfxs/W2R3QMgIez9hU0C0vLkVdsnHVFDCX/wpE6ScUtteUlyE6MQ8A9mjM6ck7W/9sRAFQrCm6WAu8o7mq2PZ/4WNTzG6Njqcc0kopbBAhYtX0VokKj4O7oLkJkbVtjpdYFpbLB6pp5Z86gz5w5CJs8GTmnT8NCKoVXjx6wsrVF8vff4/zGjVB3/W7YAOf27XHPK6/AupVDA4yd3gtkkaaZo6Mgtaj/f/vM0f21jr+4pbZWies5hYj9eiMmvfolHl76P8xY/g027D2h3ievWIFX/vuHRlIBAKkZuXj5yw24ltNw1bas/CL1OIz62MitMCSyE2aPH4R2Hs512l0d7TDvgaENnoNQbw0Ljfb0umXcq8uLGjympkz7TBISz45k7QmhUqXE7lOGLw1NgM8dgzHr49GtG2rK6l/T55aK/HxYu7ggYNAg+EVFwcrWFlf27MH5P//E3c+Tiy5fxpFPPml13MaOiYWB9ezoj7eeug8d/W6PPWjn4YyXp8dgVN+ujdaTCPHzwAv/+fWfuhE3/9LmFJbi8w3x+PrvAwCAvw+maC2GlZVfjIxG1ibxcnFE/67BmDCobiElK6kUix4ZBTtrOVwd7fDZCw/jqQn3IDy4HcKCfDBzdBS+XDgNfh7mnZHrgtSy8bLed7PzbN/gMXZeQa0JifSkuJGEr6isyDCBkAb/gQPhqKW6pktICPyjomDt6qr1eImFBezrKYKVunmz1mNyUlJQcq2B7mAzwEchIugVGoheoYHIKSyFUqmCt5uj+rnboO4hCPJxw5V6Smx7uzoit0iBIkX9gyZ/352IydEROHs1q8Hz5xeXoXOgN85pqX8xJupmb8W8yUMxqHsIth45jcLScrT3dcf4Ad01einsbOSYMrSXeuYJNZ1718G4duAXre1uoVHIPLoReWf2QVl7c/CmR9hgjYGed3L07wp7n7rjb0h8QZ5BSL6qfRZVsFew1jbSH6lMhuilS3Hi66+RceQIBKUSFlZW8B8wAD0ffxwWlpYIGTUKp37+ud7jffv2haBSIfvECcidnOASfPM6Fl+/3uB5i69d05rQmAMmFiK6VVXzTpZSKVY+Mwn//mUnjp65DNU/XWkRHf2x4KHhWPjpb1o/r0apxLGzV2Br3fBvwnbWMvzftFFY9PnvyC3SHOfQP6y9RpIQ0dEfEa2oykna2XkGwbvXGGQn1P3txjGgGzKO/onyO6ablmWnQSq3hXevsbhxYisE1e1y+HbeHdB50ssGiZsaplQpYSGx0BikN673OGxO3IxaZd0lDFztXXFPWENrx7Seyz8DaV04oLYOaycnRC1YgKqSElQUFMDW3V1jmmjoxIkounoV1w9qLrngFBQEZU0N/p4zB/jnEbZTQAB6z5kDa2dnlDewXoiNmY+xYGJhhFwc7PDmkxOQU1iKrPxieDo7wMf9ZglgVSODjZQqAcMiQ7HvZN1RzLcM69UZfh4u+N//PYodx88iOe065DIrDO7REX06B2mtwkm61yFmDuw8g5GVsAkVedcgc3CHV8+RqK0sReaRP+rsr6wqR/GVk+gzbzVyz+6D8p86Fk7te7aJ0ebGLOlyEn458AuSryRDaiFFVOcoTLtnGgI8AuDn5of/u///8O+N/0ZF9e26B+4O7oidGguZpX6LKH3yhPk/128tuaMj5I6OdbZbSKWIWrAA+ePGIePIEahqa+EZHo7zf/6J7IQEjX2L09Ox94030P7ee3Hhr7/qPY+9ry/cOpt3gTsmFkbM08WhTq9G785B2HK47qA+AJBaWKB3aCBcHe0woFswDtYzbXXK0F4I8Lr5zNDWWoYJg3rUO5aCDEMikcCn12j49Bqtsf3Iv7VPwa3Iv46qkhyNaagkroPnD+Lt396G6p/VaGtVtdh3Zh8S0xLx3sz3EOQZhIGdByKifQT2ndmH3JJcBHkGoX+n/rCU8sewKXDr1AlunToBuFnjIu9s/TPiasrLIQgC3MPCkHfmjEabla0t+j77rNn/EsC/0SbmwaG9EJ90AeX1DM4c3rszth8/gxMXrkEqtcDQiE64kl2AgpIy+Hm6YMKg7hgWad6ZsjkQBAE15SUN7lPN2R9GQyWo8L/t/1MnFXcqqyrDD/E/4LUpr6GiugJr96/FtqRtKKkogbezN/JK8jCh7wT1UupkGrStdnpL3tmzGLZ8OdL370f6gQNQ/lPHosOoUbB1vz2tuKaiAtknTkBZXQ33zp1h7+2t79ANgomFifHzdMHKZybj8w17cObKzUGa9jZyDInohAMpaSg8qjmws6OfJ759dRZLa5sQiUQCW88glOdc1rKDBew8gwwaE2mXmpWK7CLtCwEevnAY5ZXleO3n13Au4/baOtlF2Vi1fRWu5FzB8+OfN0CkpE1z1/RobF+pTAYLKysEDR2KoKH1T71PjYtDyo8/3i4JLpHAf+BA9HnmGUjlpv3zmomFCQoN8MLHz01FVn4xyiqr4OfhgsVfbqi3xPbF6zn4Nu4Q5tw/xOBxUsu163cfLv71Ub1tbp36wdrZy7ABkVY1tTUNtqsEFXad2qWRVNxp28ltuK/vfWjv1fBUYtItVU0Nzv3xB9K2b0dlQQGsnZ0RPGIEOk+aBKmV9pWZAaBd//5I/vFH9aDNu/lFRTV4fMbRozjxv/9pbhQEXNu/HxaWluj77LPN+i7Ghv1vRii/uKxJVSl93JwQ0s4TeUWKBitpbjt6ptHCW2RcvHqMgN+AKcBdXeSOAeHoOO45kaKi+nTw7gA7uZ3W9k6+nXAs9ViDn7H/7H5dh0UNEAQBBz/4AKd/+QWVBTcLBlYWFeHMr7/iwLvvQmjk56W9lxc6jR1bb5tzUBDaDxvW4PHnN27U2pa+bx8qChouYmjs2GNhRI6dvYLVmw/i4vWb05SCfd0xMyYKA8I71Nm3rLIKcitLWEqlyC9puDJcWWU1qqprYWvd9pbvNWVBw2bBu9cY5J878M8iZN3h6B8mdlh0F2uZNSb2m4gf9/5Yb/vUgVOx8Zj2f0gAoLq2/oJ2pB85ycnI0rJOyI2kJGSfOAGfXg3X5ukxcyYcfH1x8e+/UXL9OmT29ggaOhRdHnhAvdy6NgUNLLkuKJUoTEuDTQOFuYwdEwsjcfTsZbz+v43qapoAcCkzD0tX/4XXZ43DPd1DAAB/7j+J9fEnkJlXBLmVJYZEdMJ9g3rCwkKiceydPJwdYCNvuGuPjJO1kyfa9btf7DCoEY/c8whUKhX+OPqHejqpi70LHhv6GKJCo3A19ypOailsBgA92nNmliFdP3y40fbGEgsACB4xAsEjRkBQKiGR1r/4X0FqKjKPHYMgCPDu2RMeYWGwtLVFjUJ7r7SljfYFHE0BEwsjsWbzoXoTA0EA1mw+iHu6h+CrjfuwbvftedNVNbXYevQMktMy0D+sfb3TSwFgwqDuZj+9iUhMEokEM4bMwOSoyTiXcQ5WUit08euinko6OmI0Nh7bWG/p7k6+ndArmJVrDUlZ0/C4GGV1NapKSpC6ZcvN2hVKJby6d0fHsWPrnblRX1KhqqnB4Y8/RsYdScy59evh1b07/KOicGn79nrPbevuDo8uXZr5jYwLx1gYgbxihfrxR33SbxTg1KUM/BafWG97Vn4x/L1cERZUt2b98N5dWG6byEBs5baIDI5EeGC4Rn0KJzsnrJi+Ap18O6m3WUgs0L9Tfyx7aBkTfwPz7KZ9kUUAcG7fHjtefhlnfv0VxenpKM3IQOqWLdixaBEKUlM19q2tqkJJRgaqijWngJ9et04jqbjlRnIyaquqYOdVdwC2xNISEU8+qbX3w1Swx8IYNFxMEwCQeCFd66MO4Ob4jC8XTkPihXQkXkiHlVSKQT1CENLOU2O/k6nXsOlgCjLziuHl6ohxUeGIDA1o7TdoNhcHW43/Epm7QI9AfPT4R7iScwUFpQVo59YOXpzdIwr/AQNw9vffociqu66SnZcXiq5erbckd015ORK/+grD330XqpoapPz0Ey7t3Ina8nLAwgI+kZGIeOwx2Li64tIO7SvaZhw+jJEffIBLO3fi2sGDUFZVwSMsDKH33QfXkBCdflcxMLEwAu7O9mjv447LWXn1tvu6O8OukfU/apUqSCQShLTzRGV1DSylUvh7aA7++XHbEazZckj9/sK1G9h38iIeGdEXj40Z0Pov0gyfv6i9siSROQvyDEIQ65CI6tbiY8c++ww5ybcXh/Po1g29nnoK2xYu1HpsYVoaSq5fx+lffsH1Q7d/nkKlQtbx4yi6fBmDFi9GdWmp1s9QVlejtqoK3adPR/fp03XynYwJEwsjMXN0fyxbvQn1LQUyM6Y/gn09Gjw+opM/vvwjHhsPJKOmVgkAcLCV47ExAzB+YA9cycrXSCru9NP2oxgUHoKO/p71thMRmRtbNzdEL1kCRVYWynJyYOvpCQcfH1SXlUFV3fAsnYKLFzWTijtU5Ofj+uHDsJDJtH+OhQWsnZxaFLe1s7PGf40REwsjMTA8BK89OhZrthzEtZxCADd7KmbG9MewXjfLcA/qHoL9yal1jrWzlkGpVOHPfZqjzkvLq/DJb7vhaGeD8+k3Gjz/1qOnmVgQUZtj7+MDe5/b49OsbG1h5+2Nsuz6q6layGQorecRyp1unDwJ/6goXI2Pr7fdp2dPWLdwhdPhK1e26DhDYmJhRAb37IjBPTviek4hBAjw83DRGNT18rQYfGG3B9uOnVX3SnT088S/JtyD17/WPk9+7Y5jCPJx19oOoN6qnUREbY1EIkGnsWNx4uuv620PGjKk0ToVkEjQ/dFHUZCaitKMDI0mW3d3RDz5pK7CNUpMLIyQn2f9maxcZonnHxyOx8cORPqNAjjY2SDQyxXJaddRUaV9+lRqRi4GdW94QFB734YTDyKitiJk9GiU5+XhwqZNEJRK9Xa/qCj0nDULiqwsnPrpJ63H+/bqBWsnJwx/5x1c2bMHGceOQVCp4BMRgfb33guZvb0hvoZomFiYIEc7G3QLbqd+by1ruPiVpdQCo/p1xbpdCSivqvvMz1pmidH9Gp5+RUTUlnSfMQMdx4xB5vHjUNXWwqtHDzj6+QEAnAID4T9oEK7tr1uK3dbdHcEjRwK4WegqZPRohIwerbO4dixahMqiIlg7OxvtYxEmFmago58n2nk4IyO3qN72Qd1D4O5kjzeeHI9lq/9GaXmlus3eRo5XHx0DNyftax0QkW4kpCVga9JWFJQWwN/dH+N6j0MH77ol+8k42Li5ocOoUfW29X32Wdh5eODS9u2oViggkUrh27s3es6aBbmDg95iqiwqMvq1RJhYmAGJRII590cj9uu/UKvUXDzHyc4GM2NurrTXI8QfP8U+gb1JF5GVf7OORXTPTiz3TWQAX279UmPNkDPXz2D7ye2YP3Y+RvYcKWJk1BIWlpYInzYNYQ8+iIr8fMjs7c3+EUdTMbEwE327tMcHzz6An3ccx4kL6bCUWuCeHh3x8PA+8HV3Vu9nLbPCyL5cyIrIkBIvJda7EJlKUOHTzZ+id0hvuNqb7qJT5qo0MxPnNmxAxrFjgEoF74gIdL7/fjgHBan3kVpZ1Vvmuy1jYmFGwoJ88eaTE8QOg4jusj2p/nUhAKBWVYvdKbsxOWqyASOixhRfvYrdS5agpuz26tHXDhxA5vHjGPz663Dv3FnE6Iwb1wohItKzwrLCBtsLFMb9zLwtSv7+e42k4hZlVRVOfvutCBGZDiYWRER65u/u32B7oEeggSKhpqhWKJB9Uvsy9wUXL6LsRsNFB9syk0ksVqxYgT59+sDBwQGenp6YOHEizp8/L3ZYRESNGtd7HKQW9a9Y6WTrhMFdBxs4ImpIbVUV6l1f4c59KiuRe/o0Dr73HrbMm4ddr72GSzt2QHVH3Yu2ymQSi/j4eMydOxeHDx/G9u3bUVtbi5EjR6Ksnq4qIiJjEugRiAUTFkBmKdPY7mznjKVTl8LaqpFKjmRQNi4usPPUvsSB3NEROadPY8/Spcg4cgSKrCzknzuHhC+/xMH33mvzyYXJDN6Mi4vTeL969Wp4enoiISEBgwcz2yci4za021D0Cu6F3ad2o0BRAH83f9wTdg/kVg2vXEyGJ7GwQOh99yHxq6/qbW8/fDiSv/uu3l6NrOPHce3AAQS24X+XTCaxuFtxcTEAwNWVU7SIyDQ42jrivr73iR0GNUGHUaNQrVDg3IYNqK28WVTQQiZDp7FjYe3sDFVtrdZjr+7Zw8TC1AiCgAULFmDQoEHo1k17KeqqqipUVVWp3ysUCkOER0REZqDL5MkIGT0aOSkpEAQBnl27QubggNPr1jV4XHUb/7fGJBOLZ599FsnJydhfT532O61YsQLLli0zUFRERGRurGxt0a5fP41tLsHBDR7j3Ei7uTOZwZu3zJs3Dxs3bsTu3bvh98+CMNosXrwYxcXF6ld8fLyBoiQiInPlExkJh3bt6m2TWFqiow4XHTNFJpNYCIKAZ599FuvXr8euXbvQvn37Ro+Ry+VwdHRUv+xZx52IiFpJYmGBQa+8AseAAI3tVra26P/883AKbNt1SUzmUcjcuXPx008/4c8//4SDgwOys7MBAE5OTrCxsRE5OiIiakvsvbww8oMPkJOSguKrVyF3ckK7fv1gKecsH5NJLL744gsAwJAhQzS2r169GrNmzTJ8QERE1KZJJBJ4de8Or+7dxQ7FqJhMYiE0UgWNiIiIxGcyYyyIiIjI+JlMjwUREZEhleXkIDUuDnlnzkAqk8FvwAAEDR3KcRSNYGJBRER0l/wLF7B3+XLUlpert+WeOYMru3cjeulSWHHSgFZ8FEJERHSXY599ppFU3FKYloZz69eLEJHpYGJBRER0h/wLF1CakaG1/cqePRrvlVVVEFQqPUdlOvgohIiI6A5V/yxy2VC7IAhI3bIFqZs3Q5GdDUtbWwRFR6Prgw9C5uBgoEiNExMLIiKiOzgGBAASSb3LogOAU0AAkr75Bqlbtqi31ZaXI3XLFuScPo1hb73Vpsdg8FEIERHRHey9vOATGam13X/gQKTGxdXbVpKejiu7dukrNJPAxIKIiOgufZ59Fm6hoZobLSwQet99DfZmAMD1Q4f0Fpe1szNsXF1h7eyst3O0Fh+FEBER3UXu4IBhb72F3NOnkXurjkVUFOw8PXHm118bPFZZW6u3uIavXKm3z9YVJhZERERaeHTtCo+uXTW2eYaH4/Qvv2g9xis8XN9hGTU+CiEiImoG986d4akleZDZ26NDTIyBIzIuTCyIiIiaacBLLyFg0CBIpFL1NpfgYETHxsLWzU3EyMTHRyFERETNZGVri37PP4/QiRNxIzkZ9t7eaNe3r9hhGQUmFkRERM1UU1GBxFWrcO3gQQhKJQDAJSQEvf/1Lzi3by9ydOLioxAiIqJmOvjee0jft0+dVABAYWoq4pctQ3l+voiRiY+JBRERUTPknz+PnOTketuqFQqkaSme1VYwsSAiImqGGykprWo3d0wsiIiImsHCsuHhiY21mzsmFkRERM3g16/fzbLe2tr79zdgNMaHiQUREVEz2Pv4IERLESxHf3+0v/deA0dkXNp2fw0REVEL9Hz8cdh7e+Pi5s0ou3EDljY2CBw8GF0feqhNL5kOMLEgIiJqNolEgo5jx6Lj2LGorayEVCaDxIIPAQAmFkRERK1iaW0tdghGhYkFERHRXQSVCpnHj+PagQOoraiAW2go2g8fDmsnJ7FDM3pMLIiIiO6gUipx6L33kHn8uHpbVmIiLmzahMGvvQaXDh1EjM748YEQERHRHVK3bNFIKm6pLi3F4Y8/hiAIAABlTQ3S9+1D8g8/4MJff6GyqMjAkRon9lgQERHd4fKOHVrbFJmZyDt7Fla2ttj/9tuoKChQtyX/+CMin3gCwSNGGCJMo8UeCyIiojuU35Es1KcsJwcH3nlHI6kAAKG2FgmrVqHg4kV9hmf0TCqx2Lt3L8aPHw9fX19IJBL88ccfYodERERmxsHHp8H2ioIClOfl1d8oCEjdskUPUZkOk0osysrK0KNHD3z66adih0JERGaqw6hRWttcQkKgqq1t8Pji69d1HZJJMakxFqNHj8bo0aPFDoOIiMxY+2HDUJiWhrStWzW223l5of8LL+DGyZMNHm/j7KzH6IyfSSUWzVVVVYWqqir1e4VCIWI0RERkKiJnz0bwiBFI379fXcfCPyoKFlZWkA0ciJPffQdlZWW9xwYNG2bgaI2LWScWK1aswLJly8QOg4iITJBzUBCcg4LqbJfZ2aHPM8/gyCefQFAqNdqChg5Fu379DBShcZIItybkmhiJRIINGzZg4sSJWve5u8ciKSkJ0dHRSEhIQGRkpAGiJCIic1V87RrS4uJQdPUqrB0dETRsGHx79xY7LNGZdY+FXC6HXC5Xv7e3txcxGiIiMidO/v6InD1b7DCMjknNCiEiIiLjZlI9FgqFAqmpqer3ly9fRlJSElxdXREQECBiZERERASYWGJx/PhxDB06VP1+wYIFAICZM2dizZo1IkVFREREt5hUYjFkyBCY6FhTg8vKykJWVpbYYZCO+Pj4wKeRaoBkOnh/mh/eo7eZVGLRWj4+PoiNjTX7i19VVYWHH34Y8fHxYodCOhIdHY2tW7dqDEYm08T70zzxHr3NZKebknYlJSVwcnJCfHw8Z8KYAYVCgejoaBQXF8PR0VHscKiVeH+aH96jmtpUj0Vb07NnT/4lNwMlJSVih0B6wPvTfPAe1cTppkRERKQzTCyIiIhIZ5hYmCG5XI7Y2FgOIjITvJ7mhdfT/PCaauLgTSIiItIZ9lgQERGRzjCxICIiIp1hYkFEREQ6w8SC6tizZw8kEgmKiorEDoWI6sF7lIwZEws9y87Oxrx58xAcHAy5XA5/f3+MHz8eO3fu1Ol5hgwZgueff16nn9mQVatWYciQIXB0dOQPuHpIJJIGX7NmzWrxZwcFBeGjjz5qdD9eo6Yxx3u0oKAA8+bNQ2hoKGxtbREQEID58+ejuLjYIOc3dmLfn+Z+fVh5U4+uXLmCgQMHwtnZGStXrkT37t1RU1ODrVu3Yu7cuTh37pxB4xEEAUqlEpaWrb/s5eXliImJQUxMDBYvXqyD6MzLnQtM/fLLL1iyZAnOnz+v3mZjY6P3GHiNGmeu92hmZiYyMzPx/vvvIywsDFevXsXTTz+NzMxM/PbbbzqK1nSJfX+a/fURSG9Gjx4ttGvXTlAoFHXaCgsL1X++evWqMGHCBMHOzk5wcHAQpkyZImRnZ6vbY2NjhR49egjfffedEBgYKDg6OgpTp04VSkpKBEEQhJkzZwoANF6XL18Wdu/eLQAQ4uLihF69eglWVlbCrl27hMrKSmHevHmCh4eHIJfLhYEDBwpHjx5Vn+/WcXfGqE1z9m2rVq9eLTg5OWls27hxoxAZGSnI5XKhffv2wtKlS4Wamhp1e2xsrODv7y/IZDLBx8dHmDdvniAIghAdHV3nWjeG10i7tnCP3rJu3TpBJpNp/D0j8e/PW8zp+jCx0JP8/HxBIpEIb7/9doP7qVQqISIiQhg0aJBw/Phx4fDhw0JkZKQQHR2t3ic2Nlawt7cXJk2aJKSkpAh79+4VvL29hVdeeUUQBEEoKioSoqKihNmzZwtZWVlCVlaWUFtbq/7h0717d2Hbtm1CamqqkJeXJ8yfP1/w9fUVNm/eLJw+fVqYOXOm4OLiIuTn5wuCwMRC1+7+wRUXFyc4OjoKa9asEdLS0oRt27YJQUFBwtKlSwVBEIRff/1VcHR0FDZv3ixcvXpVOHLkiLBq1SpBEG7+vfLz8xPeeOMN9bVuDK9R/drKPXrLV199Jbi7uzf7/5O5E/v+vMWcrg8TCz05cuSIAEBYv359g/tt27ZNkEqlQnp6unrb6dOnBQDq31BiY2MFW1tb9W8/giAIL730ktCvXz/1++joaOG5557T+OxbP3z++OMP9TaFQiFYWVkJP/74o3pbdXW14OvrK6xcuVLjOCYWunH3D6577rmnzj9m33//veDj4yMIgiB88MEHQqdOnYTq6up6Py8wMFD48MMPm3x+XqP6tZV7VBAEIS8vTwgICBBeffXVJu3floh9fwqC+V0fDt7UE+GfgqYSiaTB/c6ePQt/f3/4+/urt4WFhcHZ2Rlnz55VbwsKCoKDg4P6vY+PD3JycpoUS+/evdV/TktLQ01NDQYOHKjeZmVlhb59+2qcj/QnISEBb7zxBuzt7dWv2bNnIysrC+Xl5ZgyZQoqKioQHByM2bNnY8OGDaitrRU7bLPTVu7RkpISjB07FmFhYYiNjW328W2Noe9Pc7w+TCz0pGPHjpBIJI3+IBAEod4fbHdvt7Ky0miXSCRQqVRNisXOzk7jc28d35Q4SPdUKhWWLVuGpKQk9SslJQUXL16EtbU1/P39cf78eXz22WewsbHBnDlzMHjwYNTU1IgdullpC/doaWkpYmJiYG9vjw0bNtSJkeoy5P1prteHiYWeuLq6YtSoUfjss89QVlZWp/3W1L+wsDCkp6fj2rVr6rYzZ86guLgYXbp0afL5ZDIZlEplo/uFhIRAJpNh//796m01NTU4fvx4s85HLRcZGYnz588jJCSkzsvC4uYtaWNjgwkTJuCTTz7Bnj17cOjQIaSkpABo+rWmhpn7PVpSUoKRI0dCJpNh48aNsLa2bvKxbZmh7k9zvj6cbqpHn3/+OQYMGIC+ffvijTfeQPfu3VFbW4vt27fjiy++wNmzZzF8+HB0794d06ZNw0cffYTa2lrMmTMH0dHRGt2jjQkKCsKRI0dw5coV2Nvbw9XVtd797Ozs8Mwzz+Cll16Cq6srAgICsHLlSpSXl+OJJ55o8vmys7ORnZ2N1NRUAEBKSgocHBwQEBCg9dx005IlSzBu3Dj4+/tjypQpsLCwQHJyMlJSUrB8+XKsWbMGSqUS/fr1g62tLb7//nvY2NggMDAQwM1rvXfvXjz00EOQy+Vwd3ev9zy8Ro0z13u0tLQUI0eORHl5OX744QeUlJSgpKQEAODh4QGpVNrkuNsaQ9yfZn99xBrc0VZkZmYKc+fOFQIDAwWZTCa0a9dOmDBhgrB79271Pk2dynanDz/8UAgMDFS/P3/+vNC/f3/BxsamzlS2uwd4VVRUCPPmzRPc3d1bPJUtNja2zrQqAMLq1atb8H/JvNU3nS0uLk4YMGCAYGNjIzg6Ogp9+/ZVjyzfsGGD0K9fP8HR0VGws7MT+vfvL+zYsUN97KFDh4Tu3bsLcrm8welsvEZNY4736K32+l6XL19u4f8p8yTG/Wnu14fLphMREZHOcIwFERER6QwTCyIiItIZJhZERESkM0wsiIiISGeYWBAREZHOMLEQ0axZsyCRSPDOO+9obP/jjz/0WgWzpqYG//d//4fw8HDY2dnB19cXjz76KDIzMzX2q6qqwrx58+Du7g47OztMmDAB169f11tcpo7X07zwepoXXk/DYWIhMmtra7z77rsoLCw02DnLy8uRmJiI119/HYmJiVi/fj0uXLiACRMmaOz3/PPPY8OGDVi7di32798PhUKBcePGsepjA3g9zQuvp3nh9TQQsQtptGUzZ84Uxo0bJ3Tu3Fl46aWX1Ns3bNjQYOEjfTh69KgAQLh69aogCDeXebayshLWrl2r3icjI0OwsLAQ4uLiDBqbqeD1NC+8nuaF19Nw2GMhMqlUirfffhv/+c9/mtXtNXr0aI3V9+p7NUdxcTEkEgmcnZ0B3Fzhr6amBiNHjlTv4+vri27duuHgwYPN+uy2hNfTvPB6mhdeT8PgWiFG4P7770fPnj0RGxuLr7/+uknH/O9//0NFRYVOzl9ZWYmXX34ZjzzyCBwdHQHcXGdCJpPBxcVFY18vLy9kZ2fr5LzmitfTvPB6mhdeT/1jYmEk3n33XQwbNgwvvvhik/Zv166dTs5bU1ODhx56CCqVCp9//nmj+wtcXr1JeD3NC6+neeH11C8+CjESgwcPxqhRo/DKK680aX9ddM3V1NTgwQcfxOXLl7F9+3Z19gwA3t7eqK6urjPIKScnB15eXs37cm0Qr6d54fU0L7ye+sUeCyPyzjvvoGfPnujUqVOj+7a2a+7WX/KLFy9i9+7dcHNz02jv1asXrKyssH37djz44IMAgKysLJw6dQorV65s8XnbEl5P88LraV54PfWHiYURCQ8Px7Rp0/Cf//yn0X1b0zVXW1uLBx54AImJidi0aROUSqX6OZ6rqytkMhmcnJzwxBNP4MUXX4SbmxtcXV2xcOFChIeHY/jw4S0+d1vC62leeD3NC6+nHok7KaVtmzlzpnDfffdpbLty5Yogl8v1Ov3p8uXLAoB6X7t371bvV1FRITz77LOCq6urYGNjI4wbN05IT0/XW1ymjtfTvPB6mhdeT8ORCIIgGCaFISIiInPHwZtERESkM0wsiIiISGeYWBAREZHOMLEgIiIinWFiQURERDrDxIKIiIh0hokFERER6QwTCyIiItIZJhZERESkM0wsiIiISGeYWBAREZHOMLEgIiIinWFiQURERDrDxIKIiIh0hokFERER6QwTCyIiItIZJhZERESkM0wsiIiISGeYWBAREZHOMLEgIiIinWFiQURERDrDxIKIiIh0pk0lFllZWVi6dCmysrLEDoWIiMgstbnEYtmyZUwsiIiI9KRNJRZERESkX0wsiIiISGdMKrHYu3cvxo8fD19fX0gkEvzxxx9ih0RERER3MKnEoqysDD169MCnn34qdihERERUD0uxA2iO0aNHY/To0WKHQURERFqYVGLRXFVVVaiqqlK/VygUIkZDRERk/kzqUUhzrVixAk5OTupXdHS02CERERGZNbNOLBYvXozi4mL1Kz4+XuyQiFpGWSN2BERETWLWj0Lkcjnkcrn6vb29vYjRELVCTQUgtRI7CiKiRpl1jwWR+RDEDoCIqElMqsdCoVAgNTVV/f7y5ctISkqCq6srAgICRIyMSM9qqxrfh4jICJhUYnH8+HEMHTpU/X7BggUAgJkzZ2LNmjUiRUVkAIobgL2n2FEQETXKpBKLIUOGQBDYJUxtkOIGUBkAWDuJHQkRUYM4xoLIVGQmiR0BEVGjmFgQmYrLnC5NRMaPiQWRqbi8D6gsFjsKIqIGMbEgMhXKaiDlN7GjICJqEBMLIlOS/AtQnCF2FEREWjGxIDJyvXv3ht+gh9H77cSb9Sx2vQnUVosdFhFRvZhYEBm57OxsZNzIQ3bJP8lEzllg70pApRI3MCKiejCxIDJFF7cD8e9ycTIiMjpMLIhM1YU44M9ngYLLYkdCRKTGxILIlOWeA35/Ajj0GVBVKnY0RERMLIhMnkoJJK8DfpkOnN3EsRdEJComFkTmoqII2PsesOFfQNZJsaMhojaKiQWRucm7AGycD2x5+eYMEiIiAzKp1U2JqBnSD918+fYEwh8EAqIAC/4uQUT6xcSCyNxlJt18OfkB3acCnWIAS5nYURGRmeKvL0RGLD09HeXl5QCA8moV0gsqW/5hxdeBfR8APz90c82R2iodRUlEdBsTCyIjdPToUYwfPx5BQUEoLCwEABSW1yLo1aOY8PkpHLvSiqml5fnAwf/cTDCSfwVqWpGsEBHdhYkFkZFZv349Bg4ciC1btkAQBI02QQA2nyrAgJVJWH8ir3UnKi8ADn16O8Hg+iNEpANMLIiMyNGjRzF16lQolUoolcp691GqAKVKwNSvzrau5+KWisKbCcavs4DME63/PCJq05hYEBmR5cuXQxCEOj0VdxMACBCwfPNV3Z28JAPYtAC4sl93n0lEbQ4TCyIjkZ6ejk2bNmntqbibUgX8lVLQugGddxNUwP6PWL2TiFqMiQWRkdi5c2ejPRV3EwRg17ki3QZSlgtkJOj2M4mozWBiQWQkSktLYdHMAlYWEqCksmk9HM1y9L9ckp2IWoSJBZGRcHBwgKqZjyBUAuBoLdV9MHkXgZNrdf+5RGT2mFgQGYl7770XEomkWcdIJMCwzs76CaiyWD+fS0RmjYkFkZEICAjAuHHjIJU2rQdCagGMD3dFgKu17oPpPBbo+5TuP5eIzB4TCyIj8vrrr0MikTTacyEBIIEEr40J1G0Aju2Asf8GohdxPREiahEmFkRGpE+fPvjll18glUq19lxILQCphQTrZndBnyAH3ZzYwhKImA5MWQ349dLNZxJRm8TEgsjITJo0CQcPHsSYMWPq9FxIJMDYbq44uKgn7o9wb/3JJBZAxxHAg98BfWcDlvLWfyYRtWlcNp3ICPXp0wcbN25Eeno6evbsicLCQrjYWiLptUjdjKmQOwChY4Cu9wOOPq3/PCKifzCxIDJiAQEBsLW1RWFhIWxlFq1PKjxCgbCJQIdhgJUeBn0SUZvXosQiLS0Nq1evRlpaGj7++GN4enoiLi4O/v7+6Nq1q65jJKLWsJAC7aOBbpMBr643n6cQEelJs8dYxMfHIzw8HEeOHMH69euhUCgAAMnJyYiNjdV5gETUQjYuQOSjwCPrgOGxgHc3JhVEpHfN7rF4+eWXsXz5cixYsAAODrdHpA8dOhQff/yxToMjohbw6QF0nQgE3QNIrcSOhojamGYnFikpKfjpp5/qbPfw8EB+fr5OgiKiZrKQAh1HAd2nAK7BYkdDRG1YsxMLZ2dnZGVloX379hrbT5w4gXbt2uksMCJqIv9+wIB5gLO/2JEQETV/jMUjjzyC//u//0N2djYkEglUKhUOHDiAhQsX4tFHH9VHjERUHwtLYNDzwOh3mVQQkdFodo/FW2+9hVmzZqFdu3YQBAFhYWFQKpV45JFH8Nprr+kjRqI2zdvbG6itgre88vZGmR0wcjnQLlK8wIiI6iERBEFoyYGXLl1CYmIiVCoVIiIi0LFjR13HpnOJiYno1asXEhISEBnJH8hkQlJ3ADvfvPlnmR0w7sObNSmIiIxMiwtkBQcHIziYg8SIDO7eWCYVRGS0mj3G4oEHHsA777xTZ/t7772HKVOm6CQoItKiUwwQ0E/sKIiItGpRgayxY8fW2R4TE4O9e/fqJCgi0qLnw2JHQETUoGYnFgqFAjKZrM52KysrlJSU6CQoIqqHRyjgEiR2FEREDWp2YtGtWzf88ssvdbavXbsWYWFhOgmKiOoRdI/YERARNarZgzdff/11TJ48GWlpaRg2bBgAYOfOnfj555/x66+/6jzAu33++ed47733kJWVha5du+Kjjz7CPffwBy61AQFRYkdARNSoZvdYTJgwAX/88QdSU1MxZ84cvPjii7h+/Tp27NiBiRMn6iHE23755Rc8//zzePXVV3HixAncc889GD16NNLT0/V6XiLRWVqzVDcRmYQW17EQQ79+/RAZGYkvvvhCva1Lly6YOHEiVqxY0ejxrGNBJiv3AuDRSewoiIga1eI6FtXV1cjJyYFKpdLYHhAQ0OqgtJ0vISEBL7/8ssb2kSNH4uDBg3o5J5HRkNmKHQERUZM0O7G4ePEiHn/88Tr/mAuCAIlEAqVSqbPg7pSXlwelUgkvLy+N7V5eXsjOzq73mKqqKlRVVanfKxQKAEBtbS1qamr0EieRXqgsAP6dJSKRWVlZNbpPsxOLWbNmwdLSEps2bYKPjw8kEkmLgmupu893K6Gpz4oVK7Bs2bI62/v1Y4EhIiKi5mrK6IlmJxZJSUlISEhA586dWxRUS7m7u0MqldbpncjJyanTi3HL4sWLsWDBAvX7pKQkREdH48iRI4iIiNBrvEQ6VV12c40QIiIj1+zEIiwsDHl5efqIpUEymQy9evXC9u3bcf/996u3b9++Hffdd1+9x8jlcsjlcvV7e3t7AIClpWWTunOIjIbEBrDk31kiMn7NTizeffddLFq0CG+//TbCw8Pr/APt6Oios+DutmDBAsyYMQO9e/dGVFQUVq1ahfT0dDz99NN6OyeRUbBo8ThrIiKDavZPq+HDhwMA7r33Xo3t+h68CQBTp05Ffn4+3njjDWRlZaFbt27YvHkzAgMD9XZOIuNg2LFMREQt1ezEYvfu3fqIo8nmzJmDOXPmiBoDkcEZeJA0EVFLNTuxiI6O1kccRNQQQWByQUQmodklvQFg3759mD59OgYMGICMjAwAwPfff4/9+/frNDgi+oeganwfIiIj0OzE4vfff8eoUaNgY2ODxMREdQGq0tJSvP322zoPkIjAxIKITEazE4vly5fjyy+/xFdffaUxI2TAgAFITEzUaXBE9A9LmdgREBE1SbMTi/Pnz2Pw4MF1tjs6OqKoqEgXMREREZGJanZi4ePjg9TU1Drb9+/fj+BgLutMRETUljU7sfjXv/6F5557DkeOHIFEIkFmZiZ+/PFHLFy4kNNAiYiI2rhmTzddtGgRiouLMXToUFRWVmLw4MGQy+VYuHAhnn32WX3ESERERCaiWYmFUqnE/v378eKLL+LVV1/FmTNnoFKpEBYWpl6Hg4iIiNquZiUWUqkUo0aNwtmzZ+Hq6orevXvrKy4iIiIyQc0eYxEeHo5Lly7pIxYiIiIycc1OLN566y0sXLgQmzZtQlZWFkpKSjReRERE1HY1e/BmTEwMAGDChAmQ3LF2gSFWNyUiIiLjZnKrmxIREZHx4uqmREREpDNc3ZSIiIh0hqubEhERkc5wdVMiIiLSGa5uSkRERDrD1U2JiIhIZ7i6KREREekMVzclIiIinZEIgiA0tlNycjK6desGC4vbHRzl5eUmt7ppYmIievXqhYSEBERGRoodDhERmQlBpYLEokUVHMxOk/4vREREIC8vDwAQHByM/Px82Nraonfv3ujbt69JJBVERET6oiorEzsEo9GkxMLZ2RmXL18GAFy5cgUqlUqvQREREZkSobZW7BCMRpPGWEyePBnR0dHw8fGBRCJB7969IZVK692XS6oTEVFbI1RXix2C0WhSYrFq1SpMmjQJqampmD9/PmbPng0HBwd9x0ZERGQSVOXlYodgNJqUWCQnJ2PkyJGIiYlBQkICnnvuOSYWRERE/1ApOMbilmYP3oyPj0c1u3yIiIjUlMXFYodgNDh4k4iIqJVq83LFDsFocPAmERFRK9VmZ4sdgtHg4E0iIqJWqsnIFDsEo9Hkkt4xMTEAwMGbREREd1EWFUJVXg4LW1uxQxFds+uPrl69mkkFERHRXWoy2WsBNLHHYtKkSVizZg0cHR0xadKkBvddv369TgIjIiIyJdXp6ZCHhIgdhuialFg4OTlBIpGo/0xERESaqi5cgMOwYWKHIbomJRarV6+u989ERER0U3liIlc5RQvGWBAREVFdyvwCVKakiB2G6JrUYxEREaF+FNKYxMTEVgVERERkqor/2gSbHj3EDkNUTUosJk6cqP5zZWUlPv/8c4SFhSEqKgoAcPjwYZw+fRpz5szRS5BERESmoOLECVReuADrTp3EDkU0TUosYmNj1X9+8sknMX/+fLz55pt19rl27ZpuoyMiIjIxhT//DJ87/t1sa5o9xuLXX3/Fo48+Wmf79OnT8fvvv+skKCIiIlNVmZyCipMnxQ5DNM1OLGxsbLB///462/fv3w9ra2udBEVERGQqevfujT6rV2PCrp3qbfmr10CoqRExKvE0uaT3Lc8//zyeeeYZJCQkoH///gBujrH45ptvsGTJEp0HSEREZMyys7ORXVYGWNuot9Vcu4bCdevgOm2aiJGJo9mJxcsvv4zg4GB8/PHH+OmnnwAAXbp0wZo1a/Dggw/qPEAiIiJTVLzhD8g7doRd375ih2JQzU4sAODBBx80eBLx1ltv4e+//0ZSUhJkMhmKiooMen4iIqJmEQTkfvgRpLFLYN25s9jRGIzJFMiqrq7GlClT8Mwzz4gdChERUZMI1dW4sfwtVF28KHYoBmMyicWyZcvwwgsvIDw8XOxQiIiImkxVUYHsN95sM8mFySQWLVFVVYWSkhL1S6FQiB0SERG1Qary8pvJxaVLYoeid2adWKxYsQJOTk7qV3R0tNghERFRG3Uruai+fl3sUPRK1MRi6dKlkEgkDb6OHz/e4s9fvHgxiouL1a/4+HgdRk9ERNQ8qtJSZL/5Jmrz88UORW+aPStEqVRizZo12LlzJ3JycqBSqTTad+3a1eTPevbZZ/HQQw81uE9QUFBzQ1STy+WQy+Xq9/b29i3+LCIiIl1Q5uUj+83l8Fn+JqRm+O9SsxOL5557DmvWrMHYsWPRrVu3Jq96Wh93d3e4u7u3+HgiIiJTVHPtGm689Ta8l7wOCxubxg8wIc1OLNauXYt169ZhzJgx+ohHq/T0dBQUFCA9PR1KpRJJSUkAgJCQEPZEEBGRyam6cAHZby6H1yuLzarnotljLGQyGUJCQvQRS4OWLFmCiIgIxMbGQqFQICIiAhEREa0ag0FERCSmqvPnkfXqa6jJzhY7FJ1pdmLx4osv4uOPP4YgCPqIR6s1a9ZAEIQ6ryFDhhg0DiIiIl2quX4dmf/3MsqPHRM7FJ1o9qOQ/fv3Y/fu3diyZQu6du0KKysrjfb169frLDgiIqK2QKVQ4MY778JxdAxcZsyAxR0TD0xNsxMLZ2dn3H///fqIhYiIqE0r2RKHipPJ8Jg/D/KOHcUOp0WanVisXr1aH3EQERERgJrMTGS+8iqcJ0+C8wMPQGLZovVCRWPWlTeJiIhMkkqFol9/Q9Zrr6PmRo7Y0TRLi9Kg3377DevWrUN6ejqqq6s12hITE3USGBERUVtXdfEiMhctgufChbAJ7yZ2OE3S7B6LTz75BI899hg8PT1x4sQJ9O3bF25ubrh06RJGjx6tjxiJiIjaLJVCgew330TZoUNih9IkzU4sPv/8c6xatQqffvopZDIZFi1ahO3bt2P+/PkoLi7WR4xERERtm1KJnH9/iIp/ikMas2YnFunp6RgwYAAAwMbGBqWlpQCAGTNm4Oeff9ZtdEREREYsPT0d5eXlAIByZS0y/vmzXqhUyPnwIyiLivR3Dh1odmLh7e2N/H9WZQsMDMThw4cBAJcvXzZ40SwiIiIxHD16FOPHj0dQUBAKCwsBACU1NRgctwWzDx7EyYICvZxXpVCgcO0vevlsXWl2YjFs2DD89ddfAIAnnngCL7zwAkaMGIGpU6eyvgUREZm99evXY+DAgdiyZUudX6gFAHtuZGNK/B7EZWTo5fyKPXug0mfPSCtJhGZ2M6hUKqhUKlj+M6923bp12L9/P0JCQvD0009DJpPpJVBdSExMRK9evZCQkIDIyEixwyEiIhNz9OhRDBw4EEqlssFeegkAqUSCX6OHoIerq87j8HjhBdgPGqjzz9WFZk83tbCwgIXF7Y6OBx98EA8++KBOgyIiIjJGy5cvV69V1RDhn9dn589hVdQAncdRkXzSaBOLFhXI2rdvH6ZPn46oqChk/NPV8/3332P//v06DY6IiMhYpKenY9OmTVAqlU3aXykI2JmVpZcBnVVnz+r8M3Wl2YnF77//jlGjRsHGxgYnTpxAVVUVAKC0tBRvv/22zgMkIiIyBjt37mz2JAUBwKFc3VfOrMnMMtpxFs1OLJYvX44vv/wSX331lcbKpgMGDGDVTSIiMlulpaUaQwGawgKAoqZWL/HU5ubq5XNbq9mJxfnz5zF48OA62x0dHVFk5HNriYiIWsrBwQEqlapZx6gA2FvpaRExIy3x0OzEwsfHB6mpqXW279+/H8HBwToJioiIyNjce++9kEgkzTpGAiDKw1P3wUilsPTy0v3n6kCzE4t//etfeO6553DkyBFIJBJkZmbixx9/xMKFCzFnzhx9xEhERCS6gIAAjBs3DlKptEn7SyUS3Ovjg3a2tjqPxTYiAhY2Njr/XF1odv/MokWLUFxcjKFDh6KyshKDBw+GXC7HwoUL8eyzz+ojRiIiIqPw+uuvY8uWLZBIJI3WsZAAmBvaWfdBSCRwnvKA7j9XR5pdIOuW8vJynDlzBiqVCmFhYbC3t9d1bDrHAllERNRa69evx9SpUyEIQr1TT6USCSQA/tO3H0a1a6fz8zvddx9cH52h88/VlRbVsQAAW1tb9O7dG3379jWJpIKIiEgXJk2ahIMHD2LMmDF1xlxIAAz19sav0UP0klRYh4XB5ZGHdf65utTkRyGPP/54k/b75ptvWhwMERGRKejTpw82btyI9PR09OzZE4WFhXCyssKme4frZUwFAFh6esJz4YuQWOpplomONDm6NWvWIDAwEBEREVzFlIiICDcHdNra2qKwsBA2Uku9JRUSKyt4LnoJUicnvXy+LjU5sXj66aexdu1aXLp0CY8//jimT58OVz0srEJERESa3GY/CXn79mKH0SRNHmPx+eefIysrC//3f/+Hv/76C/7+/njwwQexdetW9mAQERHpid3AgbAfNkzsMJqsWYM35XI5Hn74YWzfvh1nzpxB165dMWfOHAQGBkKhUOgrRiIiojbJql07uD/9r2YX5hJTi2eFSCQS9Tze5pY4JSIiooZJXVzg9cpiWOhp3Ia+NCuxqKqqws8//4wRI0YgNDQUKSkp+PTTT5Gens4pp0RERDpi6eEBn2VLYeXtLXYozdbkwZtz5szB2rVrERAQgMceewxr166Fm5ubPmMjIiJqc+ShofB8aSEsXVzEDqVFmpxYfPnllwgICED79u0RHx+P+Pj4evdbv369zoIjIiJqSxxiRsFt5kxIZDKxQ2mxJicWjz76qEkNHiEiIjIVEhtruP/radjfM0jsUFqtWQWyiIiISLes/P3huXAhZH66LwEuBuOuC0pERGTG7AYOhPucZ2BhbS12KDrDxIKIiMjQJBK4zpgOxwkTzG6YARMLIiIiA5LIZPBc8AJs+/QROxS9YGJBRERkIBIba3i/8gqsw8LEDkVvWlx5k4iIiJpOYmkJr8WLzTqpAJhYEBERGYT7nGdg07Wr2GHoHRMLIiIiPXMcHQP76GixwzAIJhZERER6JAsOhuujj4odhsEwsSAiItITiY01PF943qRLdDcXEwsiIiI9cXviCVj5+oodhkExsSAiItID2/79YD9kiNhhGBwTCyIiIh2zsLGB++zZZldVsylMIrG4cuUKnnjiCbRv3x42Njbo0KEDYmNjUV1dLXZoREREdTg9MBlSZ2exwxCFSVTePHfuHFQqFf773/8iJCQEp06dwuzZs1FWVob3339f7PCIiIjULOzt4RgTI3YYojGJxCImJgYxd1yk4OBgnD9/Hl988QUTCyIiEpW3tzeURUVwt7ICADgMG2pWq5U2l0kkFvUpLi6Gq6ur2GEQEVEbd/z4cVyfNw81mVkAAPthw0SOSFwmmVikpaXhP//5Dz744IMG96uqqkJVVZX6vUKh0HdoRETUhsnat4fM31/sMEQl6uDNpUuXQiKRNPg6fvy4xjGZmZmIiYnBlClT8OSTTzb4+StWrICTk5P6Fd1GyqkSEZE47AYOFDsE0UkEQRDEOnleXh7y8vIa3CcoKAjW/zyryszMxNChQ9GvXz+sWbMGFhYN50V391gkJSUhOjoaCQkJiIyMbP0XICIiAtSPQvw++xRW3t5ihyMqUR+FuLu7w93dvUn7ZmRkYOjQoejVqxdWr17daFIBAHK5HHK5XP3e3t6+xbESERE1xCrAv80nFYCJjLHIzMzEkCFDEBAQgPfffx+5ubnqNm9eRCIiMgK2EewJB0wksdi2bRtSU1ORmpoKPz8/jTYRn+QQERGp2fToLnYIRsEkKm/OmjULgiDU+yIiIhKd1BLy0FCxozAKJpFYEBERGTOZv1+bLop1JyYWRERErWTl17ZrV9yJiQUREVErWXp4iB2C0WBiQURE1EpSZyexQzAaTCyIiIhaycLGRuwQjAYTCyIiolaS/LOyKTGxICIiaj2pVOwIjAYTCyIiolaSSCRih2A0mFgQERG1Fnss1JhYEBERtZaE/5zewv8TRERErSSxMomltwyCiQUREVErWbq4iB2C0WBiQURE1EoSmUzsEIwGEwsiIiLSGSYWREREpDNMLIiIiEhnmFgQERGRzjCxICIiIp1hYkFEREQ6w4oeZiorKwtZWVlih0E64uPjAx8fH7HDIB3h/Wl+eI/e1qYSCx8fH8TGxpr9xa+qqsLDDz+M+Ph4sUMhHYmOjsbWrVshl8vFDoVaifeneeI9eptEEARB7CBIt0pKSuDk5IT4+HjY29uLHQ61kkKhQHR0NIqLi+Ho6Ch2ONRKvD/ND+9RTW2qx6Kt6dmzJ/+Sm4GSkhKxQyA94P1pPniPauLgTSIiItIZJhZERESkM0wszJBcLkdsbCwHEZkJXk/zwutpfnhNNXHwJhEREekMeyyIiIhIZ5hYEBERkc4wsSAiIiKdYWJBREREOsPEgkgPJBJJg69Zs2a1+LODgoLw0UcfNbrfqlWrMGTIEDg6OkIikaCoqKjF5yQyJ2LfnwUFBZg3bx5CQ0Nha2uLgIAAzJ8/H8XFxS0+rzFh5U0iPbhzgalffvkFS5Yswfnz59XbbGxs9B5DeXk5YmJiEBMTg8WLF+v9fESmQuz7MzMzE5mZmXj//fcRFhaGq1ev4umnn0ZmZiZ+++03vZ7bIAQi0qvVq1cLTk5OGts2btwoREZGCnK5XGjfvr2wdOlSoaamRt0eGxsr+Pv7CzKZTPDx8RHmzZsnCIIgREdHCwA0Xo3ZvXu3AEAoLCzU5dciMgti35+3rFu3TpDJZBrnMVXssSAysK1bt2L69On45JNPcM899yAtLQ1PPfUUACA2Nha//fYbPvzwQ6xduxZdu3ZFdnY2Tp48CQBYv349evTogaeeegqzZ88W82sQmSWx7s9bC5hZWpr+P8um/w2ITMxbb72Fl19+GTNnzgQABAcH480338SiRYsQGxuL9PR0eHt7Y/jw4bCyskJAQAD69u0LAHB1dYVUKoWDgwO8vb3F/BpEZkmM+zM/Px9vvvkm/vWvf+nlOxkaB28SGVhCQgLeeOMN2Nvbq1+zZ89GVlYWysvLMWXKFFRUVCA4OBizZ8/Ghg0bUFtbK3bYRG2Coe/PkpISjB07FmFhYYiNjdXhNxEPeyyIDEylUmHZsmWYNGlSnTZra2v4+/vj/Pnz2L59O3bs2IE5c+bgvffeQ3x8PKysrESImKjtMOT9WVpaipiYGNjb22PDhg1mc38zsSAysMjISJw/fx4hISFa97GxscGECRMwYcIEzJ07F507d0ZKSgoiIyMhk8mgVCoNGDFR22Go+7OkpASjRo2CXC7Hxo0bYW1trcuvISomFkQGtmTJEowbNw7+/v6YMmUKLCwskJycjJSUFCxfvhxr1qyBUqlEv379YGtri++//x42NjYIDAwEcHOe/N69e/HQQw9BLpfD3d293vNkZ2cjOzsbqampAICUlBQ4ODggICAArq6uBvu+RKbEEPdnaWkpRo4cifLycvzwww8oKSlBSUkJAMDDwwNSqdSg31nnxJ6WQmTu6pvOFhcXJwwYMECwsbERHB0dhb59+wqrVq0SBEEQNmzYIPTr109wdHQU7OzshP79+ws7duxQH3vo0CGhe/fuglwub3A6W2xsbJ2pbwCE1atX6+NrEpkkMe7PW1PA63tdvnxZX1/VYLhsOhEREekMZ4UQERGRzjCxICIiIp1hYkFEREQ6w8SCiIiIdIaJBZER2LNnD5c2JzJivEebjrNCiIxAdXU1CgoK4OXlBYlEInY4RHQX3qNNx8SCiIiIdIaPQoj0YMiQIZg3bx6ef/55uLi4wMvLC6tWrUJZWRkee+wxODg4oEOHDtiyZQuAut2sa9asgbOzM7Zu3YouXbrA3t4eMTExyMrK0jjH888/r3HeiRMnYtasWer3n3/+OTp27Ahra2t4eXnhgQce0PdXJzIJvEf1h4kFkZ58++23cHd3x9GjRzFv3jw888wzmDJlCgYMGIDExESMGjUKM2bMQHl5eb3Hl5eX4/3338f333+PvXv3Ij09HQsXLmzy+Y8fP4758+fjjTfewPnz5xEXF4fBgwfr6usRmTzeo/rBxIJIT3r06IHXXnsNHTt2xOLFi2FjYwN3d3fMnj0bHTt2xJIlS5Cfn4/k5OR6j6+pqcGXX36J3r17IzIyEs8++yx27tzZ5POnp6fDzs4O48aNQ2BgICIiIjB//nxdfT0ik8d7VD+YWBDpSffu3dV/lkqlcHNzQ3h4uHqbl5cXACAnJ6fe421tbdGhQwf1ex8fH6371mfEiBEIDAxEcHAwZsyYgR9//FHrb15EbRHvUf1gYkGkJ1ZWVhrvJRKJxrZbI8tVKlWTj79zrLWFhQXuHntdU1Oj/rODgwMSExPx888/w8fHB0uWLEGPHj04XY7oH7xH9YOJBZGJ8vDw0BgoplQqcerUKY19LC0tMXz4cKxcuRLJycm4cuUKdu3aZehQidqktnqPWoodABG1zLBhw7BgwQL8/fff6NChAz788EON33Q2bdqES5cuYfDgwXBxccHmzZuhUqkQGhoqXtBEbUhbvUeZWBCZqMcffxwnT57Eo48+CktLS7zwwgsYOnSout3Z2Rnr16/H0qVLUVlZiY4dO+Lnn39G165dRYyaqO1oq/coC2QRERGRznCMBREREekMEwsiIiLSGSYWREREpDNMLIiIiEhnmFgQmbm7F08iIuNibvcoEwuiZsjOzsa8efMQHBwMuVwOf39/jB8/vlnrAzRFfasi6tOqVaswZMgQODo6mtUPOGp7zPEeLSgowLx58xAaGgpbW1sEBARg/vz5KC4uNsj5m4t1LIia6MqVKxg4cCCcnZ2xcuVKdO/eHTU1Ndi6dSvmzp2Lc+fOGTQeQRCgVCphadn627i8vBwxMTGIiYnB4sWLdRAdkeGZ6z2amZmJzMxMvP/++wgLC8PVq1fx9NNPIzMzE7/99puOotUhgYiaZPTo0UK7du0EhUJRp62wsFD956tXrwoTJkwQ7OzsBAcHB2HKlClCdna2uj02Nlbo0aOH8N133wmBgYGCo6OjMHXqVKGkpEQQBEGYOXOmAEDjdfnyZWH37t0CACEuLk7o1auXYGVlJezatUuorKwU5s2bJ3h4eAhyuVwYOHCgcPToUfX5bh13Z4zaNGdfImPTFu7RW9atWyfIZDKhpqam+f+j9IyPQoiaoKCgAHFxcZg7dy7s7OzqtDs7OwO4+RvKxIkTUVBQgPj4eGzfvh1paWmYOnWqxv5paWn4448/sGnTJmzatAnx8fF45513AAAff/wxoqKiMHv2bGRlZSErKwv+/v7qYxctWoQVK1bg7Nmz6N69OxYtWoTff/8d3377LRITExESEoJRo0ahoKBAf/9DiIxMW7tHi4uL4ejoqJMeS50TO7MhMgVHjhwRAAjr169vcL9t27YJUqlUSE9PV287ffq0AED9G0psbKxga2ur/u1HEAThpZdeEvr166d+Hx0dLTz33HMan33rt5o//vhDvU2hUAhWVlbCjz/+qN5WXV0t+Pr6CitXrtQ4jj0WZM7ayj0qCIKQl5cnBAQECK+++mqT9jc09lgQNYHwT+X7W8soa3P27Fn4+/tr/PYSFhYGZ2dnnD17Vr0tKCgIDg4O6vc+Pj7IyclpUiy9e/dW/zktLQ01NTUYOHCgepuVlRX69u2rcT4ic9dW7tGSkhKMHTsWYWFhiI2NbfbxhsDEgqgJOnbsCIlE0ugPAkEQ6v3Bdvd2KysrjXaJRAKVStWkWO7s5tX2w1RbHETmqi3co6WlpYiJiYG9vT02bNhQJ0ZjwcSCqAlcXV0xatQofPbZZygrK6vTfmt6ZlhYGNLT03Ht2jV125kzZ1BcXIwuXbo0+XwymQxKpbLR/UJCQiCTybB//371tpqaGhw/frxZ5yMydeZ+j5aUlGDkyJGQyWTYuHEjrK2tm3ysoTGxIGqizz//HEqlEn379sXvv/+Oixcv4uzZs/jkk08QFRUFABg+fDi6d++OadOmITExEUePHsWjjz6K6Ohoje7RxgQFBeHIkSO4cuUK8vLytP6mZGdnh2eeeQYvvfQS4uLicObMGcyePRvl5eV44oknmny+7OxsJCUlITU1FQCQkpKCpKQkDgAlk2Ku92hpaSlGjhyJsrIyfP311ygpKUF2djays7OblNwYnFiDO4hMUWZmpjB37lwhMDBQkMlkQrt27YQJEyYIu3fvVu/T1Klsd/rwww+FwMBA9fvz588L/fv3F2xsbOpMZbt7gFdFRYUwb948wd3dvcVT2WJjY+tMnwMgrF69ugX/l4jEY4736K32+l6XL19u4f8p/ZEIwj8PgIiIiIhaiY9CiIiISGeYWBAREZHOMLEgIiIinWFiQURERDrDxIKIiIh0hokFERER6QwTCyIiItIZJhZERESkM0wsiIiISGeYWBAREZHOMLEgIiIinWFiQURERDrz/wWebCGsmz7dAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_multi_groups = dabest.load(df, id_col = \"ID\", \n", + " idx=((\"Control 1\", \"Test 1\"),\n", + " (\"Control 2\", \"Test 2\")))\n", + "fig5 = my_multi_groups.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "de81e2e4", + "metadata": {}, + "source": [ + "Create a shared control Cumming plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7d518b5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_shared_control = dabest.load(df, id_col = \"ID\",\n", + " idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\"))\n", + "fig6 = my_shared_control.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "c80ba34f", + "metadata": {}, + "source": [ + "Create a repeated meausures (against baseline) Slopeplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d46fd3a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_rm_baseline = dabest.load(df, id_col = \"ID\", paired = \"baseline\",\n", + " idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\"))\n", + "fig7 = my_rm_baseline.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "4eaf4362", + "metadata": {}, + "source": [ + "Create a repeated meausures (sequential) Slopeplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b6a3727", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OcnIyCxYsoKqqipMnT2JiYsKWLVuG7CGMlIGBAe7cucPNmzcRBIGZM2cyffp08UMeGxuLs7Mzhw8f5osvvmDTpk0vpNk0JSUFhULB+fPnsbGxYeLEiYSHh3P37l0yMzN58OABc+bMYcqUKd+b0RtGHk9xcTEnTpwAwMnJiXXr1hn0HTY0NHD48GFCQ0PFTMRIqK6u5tKlS7S2tooG146Oji/lObxKTIQfWCPLu+++i6urK//+7//O7NmzmTRp0mNTiyqVCpVKJf4/Pz+f1NRUcnNzh3VON/LyEQSBq1evkpWVxdy5c0lOTiY9PZ1bt24RHh7OypUrn8lPTr9KvXbtGgqFgilTpjBr1qzHVhF2dXVx8OBB5HI569atIzQ09HmfmtgD9ODBAwNPyN7eXtLT07l//z4eHh4sWrSIsLCw5348I2OT0tJSjh49iqOjI11dXSxevJjp06eLP29tbWXPnj14eXmxZcuWETnZd3R0cPnyZcrKyggICGDx4sX4+fm9zKfxSvlBRWSHDx8mLy+PnJycER3/61//ml/96lcv+ayMjBSdTse5c+fIzc1lyZIlhIeHi31YixYtYtq0aaPO7wuCwMOHD0lLS6O9vZ2JEycyd+7cp7puuLi4sGvXLo4ePcrXX3/N22+/zeTJk5/n6WFiYsLy5cvp7+/nyJEjbNu2jYCAAOzt7Vm+fDmJiYlcvHiR/fv3ExERwcKFC59r/8/I2KOsrIyjR4/i6+uLRCIhLi7OYJ6dfhyLi4sLGzdufKqI9ff3c/36dbKzs3F0dGTt2rVERUW9kftgT+IHE5E1NDQwZcoULl++LA6cM0Zkbw5arZaTJ09SXFzM8uXLMTEx4fz58zg4OLBmzZpnsuGpq6vjypUrNDY2Eh4ezrx580Z9PzqdjosXL5Kdnc306dNZsGDBc6f+BgYG+Prrr2ltbWXHjh14eXmJPxMEgZKSEq5cuYJcLmfq1KnMmjXrezma44dGeXk533zzDcHBwUilUuzs7Ni5c6coVj09PezevRszMzN27tz5xJ5DrVbLvXv3uHbtGlqtVkyRm5s/PXbp6+vD1tb2jRK7H4yQffvtt6xatcpg81+r1WJiYoKpqSkqleqphQF5eXlMnjzZKGSvmIGBAY4ePUpVVRXLly+nsrKSwsJC4uPjWbJkyag3qVtbW0lLS6O8vBxfX1/mz5//3KnB7OxsLly4wLhx43jnnXeeu19NqVSyd+9e+vr6DBxK9AwMDHD79m1u3ryJhYUFc+fOJT4+3rh/9oZSWVnJoUOHCAsLQ6VSIZVK+fDDD0XHeoVCwZ49e1Cr1ezcufOJTvYVFRVcunSJjo4O4uPjmTt37ojT7RqNhk8//VSsmH1T+MEImVwup66uzuB7O3bsYMKECfzsZz8bUSe8UchePSqVikOHDtHU1CRGwwqFgqVLlzJx4sRR3dfgUnoXFxfmzZv3QtMslZWVHD16FGdnZzZt2vTcYzN6e3vZvXs3ADt37hz2YiSXy7l69SoFBQV4e3uzePFiA5d/I2Of6upqDh48SGhoKM7Ozty7d49t27aJf0eVSsVXX31Fd3c3O3fufGw6ua2tjcuXL1NZWUlwcDCLFy/G29t7VOeSlpbGrVu3+PDDD/H09Hzep/bK+MEI2XA8LbX4KGNJyCoqKrCzs8PLy+t76wKhUCg4cOAA7e3tjBs3jpKSEnx9fXnnnXdG5Rw/uJTe2tqa1NRUEhISXsrr1tbWxsGDB9FoNGzcuPG5N9RlMhlffvkldnZ2bN++/bEpxKamJi5cuEBjYyORkZEsXLjQ6K7/BlBbW8uBAwcICgoiKiqK06dPs2TJEqZOnQp8F3kfOHAAiUTC9u3bh0199/X1ce3aNXJzc3F2dmbhwoVERESMeoHW1NTEF198wZw5c5g1a9YLeX6vCqOQvaFC9sc//pGuri7Mzc3x8fHB398fPz8//P39cXJyeqPy28Mhl8vZt28fXV1dODg4IJPJSElJYfbs2SMWoEdL6WfMmGFQSv+y6Ovr4/Dhw0gkElatWkV0dPRz3V9bWxt79uzBw8ODrVu3PnaDXxAECgsLuXr1Kn19fSQnJ5OSkjJmbLmMGFJXV8fXX39NYGAgqamp7Nu3j5iYGLHpWavVcuTIEaqrq9m6desQtw2tViu2ZwDMmjWLpKSkEe2DPYpGo+Gzzz7DzMyM9957741bHP+ghWy0jCUh02g0tLS00NjYSGNjI01NTXR1dQFgb28vipq/vz++vr5v1MWsq6uLffv2IZFIsLKywtHRkdWrVxMSEjKi3x9tKf3LQKPRcOrUKQoLC5k7dy4zZ858rsVFQ0MD+/btIyQkhPXr1z/xQqNWq8nKyiIrKwtra2vmz59PXFzcG7+4+T7R0NDA/v378fPzY8WKFezZs0cs7jA3N0cQBLG4aePGjQZ9kYIgiA3zMpmMyZMnM3v27Od6f6enp3Pz5k0+/PBDg+KiNwWjkI2CsSRkw9HX1yeKmv6rSqXCxMQEDw8Pg6jNw8NjTBYGSKVS9u7dS1VVFc7OzsTFxbFixYoROWg8WkofGxvLnDlzXluKTRAEMjMzycjIIDY2luXLlz/TalmPviAgOjqaVatWPVWYuru7uXLlCkVFRfj6+rJ48eI3zkPv+0hjYyP79+/H29ubDRs28M033yCVSvnggw9wcnJCEAQuXLhATk4Oa9asMYjoW1pauHjxIrW1tYSHh7Nw4cLn3stqbm7miy++IDU1ldTU1Od9eq8Fo5CNgrEuZI8iCALt7e0G4tba2oogCFhaWuLr62sgbq96PtejNDc381//9V9UVFQQHh7OsmXLSExMHFEk8SJK6V8WRUVFfPvtt/j4+LBhw4bnWjkXFRVx/Phxpk6dyqJFi0b02tTX13Px4kWam5uJiYlhwYIFz12IYuTZaG5uZt++fXh6erJ582bS09PJycnh3XffFYdgpqenk5mZybJly8TexMFN8W5ubixatOiFmEprtVo+++wzTExMeP/999+4lKIeo5CNgjdNyIZDrVYjkUgMUpI9PT3Ad1Y4g1OSPj4+I3INeBHU1tbyu9/9jqamJmbNmsWmTZtGlOJ4GaX0L4PGxkYOHz6Mubk5mzZteq5VdE5ODufOnWPu3Lkj3pTXz5m6evUqKpWK5ORkZsyY8Ub7671pSCQS9u3bh5ubG1u3bqW0tJRvv/2Wt99+m8TERABu3brF5cuXWbBgATNmzECj0XD79m1u3LiBmZkZc+bMYfLkyS9McDIyMrhx4wYffPDBqCscxxJGIRsF3wchG46enh6DqK25uZmBgQFMTU3x8vIyEDc3N7cXvtdSWFjIP//zP6NSqdi2bRvLli17qoDKZDKuXbv20krpXwYymYxDhw4hk8lYu3btM/tBAly/fp2MjAyWLl06qvlTKpWKmzdvcvv2bWxtbZk/f/4b63j+JtHa2spXX32Fs7Mz27Zto6Ojgz179hAbG8uyZcswMTEhLy+P06dPM3PmTObOnUtxcTFXr16lp6eHpKQkUlNTX6hxtEQi4fPPP2fmzJnMmTPnhd3v68AoZKNgLAlZZ2cnjo6Oz7Xn8jh0Oh1tbW0G4iaVSgGwtrYWhU3/9Xkc4K9evcrvfvc7nJ2d+fnPf/7UiQQKhYIbN26QnZ2NjY3NSy2lfxmoVCqOHz9ORUUFS5YsISkp6ZnuRxAELl26xN27d3nnnXdGPRF48Awqf39/lixZ8r3y3htLtLW1sXfvXpycnNi2bZuYznN0dGT79u2Ym5tTUlLC0aNHmTJlCnFxcVy+fJn6+vqXZkWm1Wr5/PPPEQSBDz744I35/DwOo5CNgrEkZH/605+QyWT4+PgQEBBAQEAA/v7+L83JWqlU0tzcbJCS7OvrA77zHdRHbH5+fnh7ez9VYHU6HV988QWHDh0iKiqKf/qnf8LV1fWxx7+uUvqXgU6n48qVK9y+fZukpCQWL178TIU3T6psGym1tbVcvHiRlpYW4uLimD9//mvfK/0+0d7ezt69e7G3t2fbtm1YWVmxb98+Ojo6+OCDD3B0dBSLeIKDg7Gzs+PBgwd4eXmxaNGil5Ymv3btGpmZmbz//vtjZi/5eTAK2SgYS0ImkUhoaGgQbzKZDPhun0svbAEBAS+tYVoQBGQymRixNTY2IpFI0Gq1mJmZ4ePjYxC5ubi4iOmr7u5ufvOb35CVlcWiRYv4u7/7u8ee46Ol9ImJicycOfOVltK/LO7du8f58+cJDQ1lzZo1z+SXqNVq+eabb6ipqeHdd9/F399/1PcxeD7VwMAAKSkpTJ8+/ZXtj35f6ejoYO/evdjY2PDuu+9iZ2fH+fPnyc3NZfv27QQEBNDQ0MDu3bsZGBjAysoKa2vrl2431tLSwmeffUZKSgpz5859KY/xqjEK2SgYS0L2KHK5nMbGRlHYmpub0Wq1WFhY4OvraxC1vSwR0Gq1Ym+bXuA6OzsBsLW1xd/fH7VazYULF+jo6GDHjh1s2rRp2P2ZsVZK/7Korq7myJEjODo6snHjxmd6fgMDA+zfvx+pVMqOHTueuZBEqVSSmZnJ3bt3cXBwYMGCBWN+33Gs0tnZyd69e7GysuLdd9/F3t6e+/fvc+rUKbEaUSKR8K//+q+0trYyYcIEZsyYwcyZM1+qAbQ+pajT6fjggw9eytbE68AoZKNgLAvZo2g0GrE6US9ucrkcAFdXV4Oo7WX2lCkUCpqamqitreXChQtkZGSgUqmYMmUKSUlJBilJLy8vTE1Nx3Qp/cugvb2dAwcOoFar2bBhAwEBAaO+D6VSyZ49e1AoFOzatQtnZ+dnPp/Bs6uCgoJYvHjx9/r1f9F0dXWxd+9ezM3N2b59Ow4ODjQ2NrJnzx4mTZrEsmXLePDgAf/4j/+ISqViw4YNvPXWW69kkZaZmcm1a9d477338PX1femP96owCtkoeJOE7FEEQaC7u5uGhgZR3FpaWtDpdFhZWeHn52cQtb3IVWFraytHjhzhzp072NnZsWbNGry8vMSUZGtrKzqdDpVKRUdHB/39/YSFhbFq1SpiY2N/EBGBQqHgm2++obGxkRUrVjy16GU4RmIyPBqqqqq4dOkSUql01C7qP1S6u7vZs2cPpqambN++HUdHR+RyOZ999hnOzs6sWLGC8+fPc+DAAVxcXPjlL3/JhAkTXsm5tba28tlnn5GcnMy8efNeyWO+KoxCNgreZCEbDrVaTXNzs4G4KRQKAycQvbg9S9m9IAjk5ORw4cIFGhoacHNzY/PmzUMu0lKplJMnT5KdnY1Wq8XT0xNra2tMTExwcHAwqJD09fV9Iws8RoJGo+Hs2bPi3LvZs2eP+jXv6uriyy+/xMHBgXffffe5FyQ6nY579+6RkZGBTqdj1qxZTJ069XuTknqR9PT0sGfPHgC2b9+Ok5MTWq2WvXv30t7eTlRUFDk5ORQWFhIeHs4vfvGLV9aYrtVq+eKLL9BoNHz44Yffu7+fUchGwfdNyB5FEAQ6OzsNhK2trQ1BELCxsRGjtYCAAPz8/J4oKAqFglOnTlFSUkJ/fz+Ojo6sX7+eiIgIg2MeV0ovl8sNrLaamppQq9WYmJjg6elpIG7u7u5j0m7rWRAEgaysLK5evUp0dDQrV64cddFFa2sre/bswcvLiy1btryQoo3+/n6uXbtGTk7OczmsjwRBENBoNKjVatRqNc7OzmM+KpfL5ezZswedTsf27dvF1O7p06e5cOECbm5umJubI5fL8fDw4P3333+l071v3LhBeno677333veyzcIoZKPg+y5kw6FUKmlqajIQN71/o7e3t0HUpr/g1NTUcOLECZRKpXgB2rhxo2j6+yyl9DqdDqlUalAlKZVKEQQBKysr0W5LL3BvegqstLSUEydO4OnpycaNG0f9fOrr69m/fz+hoaGsX7/+hQm9VCrl0qVLVFZWEhoayqJFi/D09ESj0aBSqVCr1eLXx/37aT9Xq9XodDrxMceNG8fGjRvH7GKlt7eXvXv3olar2bFjh7jXdfr0aT777DP8/PyYO3cu7e3tyOVytm/f/kpdNNra2vj000+ZPn068+fPf2WP+yoxCtko+CEK2aMIgoBUKhULSBobG2lvbwe+q0zs6elBIpEwbtw4LCws0Gg0bN68GX9//xdeSq9SqWhubjYQt97eXgCcnZ0NHEm8vb3fuHLy5uZmDh06hKmp6YgtuwZTUVHBoUOHiI2NFUeDwHd/w4GBgWcWGpVKRWNjI0VFRfT09ODl5UVwcPATX19LS0ssLS2xsrJ64r8f/V5fXx9nzpxh2rRpY3JicV9fH3v37kWpVLJjxw5cXV3p7Ozk0KFDHDt2jIkTJ/J//9//N1lZWdTU1LBt27ZnKuZ5VnQ6HV9++SUqlYqPPvroe5dS1GMUslFgFLLhUSgUFBcXc/jwYaqrq3F0dKStrQ2dTseiRYuIiopCrVZTXl5Ob2/vSyulFwRBtNvSpySbm5vRaDSYmpqKEaRe4FxdXcd8yqqnp4eDBw/S1tbG0qVLCQoKGpXoVFVVcffuXQIDAwkJCREF7Gkf+5GIjrm5OVVVVTx48ABzc3OmT5/O5MmTsbW1HfI7z/M63717lwsXLrB8+fIx9blTKBR89dVX9PX1sWPHDuzs7MjMzCQzM5MHDx4wZcoU/sf/+B+cOXOGkpKSZ25afx5u3rxJWloau3bteqYewzeF76c8G3mlVFdXc/XqVQICAti+fTtXrlxBLpeTnJxMZWUle/fupaWlBVdXV+Li4tDpdJSXl4uR0otq2DYxMcHJyQknJydx9IVWq6W1tVWM2qqrq8nOzgbAxsbGoGnbz8/vuey29AiC8NTU2WiiH6VSSUlJCenp6YSHh+Pn52cgDCYmJqJgPBrdREZGYm1tTUFBAVZWViQnJz82+hl8HyMVnnnz5tHX10dGRga5ublIpVIWLVr0Qku7k5KSaGtr49y5c7i5uYku8a+T/v5+9u3bR29vL9u2baO6upqMjAzx7zdz5kw++ugjLl++TFFR0XN7az4LUqmUjIwMpk+f/r0WMTBGZKPCGJEZom9uvn//PjExMSQmJnL06FGsra1ZtGgR2dnZVFRU4Ovry/Tp0zE3N3+tDdt6+vv7xQISffTW398PgJubm0FKUu+MotFoKCgooL29fURC9CQGC8/TRGXwv+/du0dBQQEJCQksXrxYjHwsLCyeKjzXrl3j2rVrBqNBXjStra1cvHiRmpoawsPDWbRoER4eHi/kvrVaLfv376etrY3333//tTbG60Wsu7ub1NRUUcDj4uJQKpVUVlayY8cOHj58yI0bN15LJKlPKSqVSj766KNRp9UFQRjz2YrBGIVsFBiF7P8gkUg4duwYcrmct956Czc3Nw4ePIiFhQU+Pj6Ul5c/0ZVeP+F6sM3W62jYhu8+tF1dXQYpyZaWFrRaLaampgwMDNDS0kJPTw+hoaEEBwdjbW09KiEa/L2RCM/juH//PmfOnCE4OJh169aNuLx+8LDGtWvXEhUV9UyPP5LHGTy9ODExkdmzZ78Q13aFQsEXX3yBubk5u3btei1Tz5VKJfv376ehoQEvLy+kUqnYNN7Y2Mi5c+dYuXIlfX19XLlyhYULF5KcnPzKz1Nf+bpz584R7ckJgkBraytlZWWUlZURGRnJzJkzX8GZvhiMQjYKjEL23Rv+zp07XL16FU9PT9asWUNPTw9fffUVPT09ODg44ODgMGpXen3D9mAnklfVsD0carWa69evc/78eUpKSmhvb0cQBLy8vMReqoSEhOdy0HhWamtr+eabb7Czs2PTpk1PNFsejCAInDhxgpKSEjZv3vxS57ZpNBru3r1LZmYmpqamzJkzhylTpjz3gkQqlfLFF18QFBTEhg0bXmklo0ql4ssvvyQnJwd3d3f8/f1ZsGABkZGR1NfX89VXX5GYmIinpydnzpxh1qxZr8XLsL29nf/6r/8iMTHxiQUyGo2Guro6Uby6u7uxsrJi3LhxxMfHExYW9grP+vkwCtko+KELWV9fH99++y0VFRVMnz6defPmUVpayh//+Ed6enqYOHEis2bNemGu9AMDA2LDtv6mUCgA8PDwMIjannVOmkajobOzE4lEQktLC62trRQVFVFQUEBLSwsKhQKdToeNjQ22trb09fURFhbGhAkTsLOzIywsjClTpjB+/PhXelHt6Ojg4MGDKBQKNmzYMOJ9I61Wy+HDh6mrq+Pdd9996T1Fgycbe3h4sGjRoue+QFZUVHDw4EGSk5NZsGDBCzrTJ9Pf388///M/k5eXx6RJk1i6dKnYGN7T08Onn36Kh4cHCQkJnDx5ksTERJYsWfLK03M6nU60KhsupahQKKisrKSsrIzKykpUKhXOzs5EREQQERFBUFDQGznSxShko+CHLGRVVVWcPHkSQRBYtWoVoaGhHD16lN27d4vDAmfPnv1S97f0DduDo7bBDduD97WcnJwYGBigt7eXzs5OWltbkUqlSKVSOjs76erqoru7m56eHrRaLYIg0N/fT3d3NzqdDgsLC6ytrfHy8mLKlClER0djZWXF119/TXV1Nd7e3sTExIgXKi8vL+Lj419plNbf38+RI0eor69n2bJlTJo0aUS/NzAwwL59+2hvb2fnzp0vbB/rSUgkEi5evEhdXd0LmbF1+/ZtLl26xMqVK0f8vJ8FQRAoKiri3/7t32hpaWHLli2sXbtWfJ9rNBr27NlDb28v8+bN49SpU0RHR7Nq1arXssd0+/ZtLl++zI4dOwgMDAS+W/SUl5fz8OFD6uvrEQQBPz8/Ubw8PT3fqP2w4TAK2Sj4IQqZVqslPT2drKwswsLCWLlypWiAmp2dzdSpU/m7v/u7l+pSoNVqUSgU9PX1Gdx6e3vp6uqiqqqK2tpasUm6v78fjUaDmZkZZmZmWFpaYm9vj62tLba2tri4uODq6oqrqyseHh7odDpqa2vp7e0VI0lnZ2dSU1OZMmUKZWVlnD9/nuzsbMzMzOjv70elUuHu7k5YWBj9/f0olUoGBgZwdnYmNjaWyZMnv5IoTavVcu7cOfLy8khJSWHevHkjuij19/ezZ88elEolO3fufCXiKwgCJSUlYlXr1KlTmTVr1jOliQVB4MyZMxQUFPDuu++KF+0XSWtrK+fOneP06dNYWFjwP/7H/zAolBEEgVOnTlFUVMSiRYu4fPkyoaGhrFu37rVENR0dHXzyySckJCQQExMjpgzb29sxNzcnNDSUiIgIxo8f/72bOWcUslHwQxOyjo4Ojh8/Tmtrq+hAn5aWxq1bt5DJZLzzzjuPHcPyJPTl6Y+K0qNCpf++TCZDqVQa3DQaDYIgiPPPLCwssLS0xNbWFhsbG0xNTcXGX41Gg42NDW5uboSEhBAYGEhAQABarZbMzEyqq6sxNzdnYGAAU1NTEhISCAkJ4ebNm9y8eZOmpiZsbW2Ji4ujoaFBdGjQl/vHxMSg1Wrp7u4Wh41aW1sTGhrKjBkzSEhIeKmeeoIgcPv2ba5cucKECRNYtWrViFK7crmc3bt3Y2pqys6dO1/ZjLeBgQFu377NzZs3sbCweOb5W1qtVows33///Rcmxn19faSnp3Pv3j2qq6txd3fnJz/5CcHBwQbH6fvbZs2aRXZ2Nt7e3mzZsuW1NB0rlUp+97vfUVtbS2hoKGq1Gjs7O8aPH09ERAShoaHfW49SMArZqPghCVlBQQHnzp3D3t6e1NRUioqKKC8vp6enB51Ox8qVKw1MbXU6nRg1PU6UBv9Mo9EA312EVSqVaHulvy+tVotGo2FgYECMqiwsLHB0dMTT0xM3NzecnZ1xcnLC2dlZvA23ulcoFGI6srGxkdLSUioqKujs7MTV1RUnJyesrKzw9/fH2dmZ+/fvU1dXh6mpKZGRkSxcuJA5c+ZgY2NDTU0NBw4coKqqCltbW7RaLebm5kRHRyOVSmlpaUEqldLV1YVSqcTU1BQvLy9mz57N/PnzGTdu3EuL0srKyjh+/Dju7u5s3LhxRKvuzs5Odu/ejaOjI+++++4rrQTs6ekhLS2NgoICvL29Wbx48RCxeBoKhYLPP/8cS0tLdu7c+VznP7hABb4r+DE1NWXLli2ivZqe2tpa9u3bR2RkJLW1tTg5Ob3y108ul4tRV2ZmJmVlZcybN4/p06cTERGBn5/fmLX1etEYhWwU/BCETKVSce7cOQoKCggJCUGn01FUVIS1tTU6nY6WlhZiY2MJDAw0EKz+/v4hbhGWlpZidKRHL1D6viuVSoW5ublYkm5nZzesQOm/9zwXCn2DaHFxMYIgoFAoePjwIe3t7SgUCtEb0t/fn9TUVJYvX87EiROHpImam5vZu3cveXl5xMbGYmlpibOzM1u2bEGhUCCRSKivryc/P5/CwkKqq6vp6enB0tISf39/5syZw8KFCxk/fvwLj4JaWlo4ePAg8J2/5UjmiLW0tLB37158fHzYvHnzK48oGhsbuXjxIo2NjURFRbFgwYJR9Ym1tbXxxRdfEBISwoYNG54pQ1BaWsqVK1fo7u4mISGB9vZ2Ghsb2bRp05Dqzu7ubj799FMcHBxQKBRYW1uzY8eOF9JM/7TzHFwi39zcjKmpKa6urhQWFjJv3jzWrVv33I+jUqme243lVWMUslHwpgvZ4KhpuEipoaGBmzdvIpPJsLS0RC6XY2FhQWBgIN3d3bS3txMXFydegO3t7cUIaHAEpVarUSqV9Pb20tPTYyBw9vb2wwqU/uvLSH90dnZy7do1CgsLsbe3RxAECgsLqaurE9OSLi4uhIWFERwcLF4wNBqNQcO2v78/YWFhWFhY0NHRwV/+8hdu377NokWLUCqVODk5sW3bNoMLmiAISCQSbt++zbVr18jNzaW1tRVzc3N8fHxISEhg8uTJ+Pn54evri4+PD/b29s91EZHL5Rw+fJi2tjbeeeedEc27qqurY//+/YSHh7Nu3bpXvpLX/02uXr2KQqFg+vTppKSkjHjhUl5ezqFDh5gxY8aojHGbm5u5dOkSdXV1jB8/nnnz5nHt2jXKy8uHtZQaGBhg9+7ddHd3Y25uLqZlHR0dR/V8R4pWq6W2tnbYEvnx48cTHh7ON998Q09PDz/60Y+e+/Ojf376gbZvCkYhGwVjUcge3Wt6UmpPoVAMiZosLCywtbWlpaWFiooK4DvzXwcHB5KSkkhISODq1atUVVUxc+ZMPD09kclk4q23t1e8T/38sEcFavD/X+Vqv7u7m8zMTPLy8kR/wfv37yORSLCysiIkJISYmBjmzJlDXFycgcP84xq2/fz82LZtG1ZWVsjlcv7t3/6Nu3fvsnXrVlpaWnB0dBwiZoNRqVQ8ePCAU6dOkZWVRWdnJzY2Nvj4+BASEoKjoyP29vb4+PgY3JycnEYlbgMDA5w8eZLS0lLmz59PcnLyU3+/vLycw4cPExcXx/Lly1/LilytVpOVlUVWVhbW1tbMnz+fuLi4EZ3LrVu3uHz5MqtWrSIuLu6Jx8rlcjGtqW8LCAkJ4fjx4zx8+JD169czfvx4g98RBIGTJ09SWFgoZhp27tw54j6+kdLf309FRcWISuT1+3Tbt28fdVr2UQRB4NixY5SXl7Nr165X6tD/vBiFbBSMJSHbs2cPzc3NDAwMGHzfxMQEW1tb7Ozshtzs7e2HfE8ul3PgwAFu3bqFWq3G3t6egIAAfHx86O3t5e7du3R3dxMdHY27u7voZThcVOXk5DQmelB6e3vJzMwkPT2drq4udDodNTU1tLe34+joSEJCAgsXLiQhIWGIb+HjEASBxsZGDhw4gKenJ1u2bMHS0pL+/n5+8YtfUFBQwI9//GMqKiqeKmZ6lEolN27c4NKlS9y/fx+1Wo2vry+RkZH4+/uLCwX4bnHxqLi5uLg88dwFQSAjI4PMzEzi4+NZunTpU/8+BQUFnDx5khkzZryyHq3h6O7u5sqVKxQVFWFra0twcLB48/DwGPZ5C4LA6dOnefDgAdu3bx/W0WJwoYm5uTlz584VP8snT56kuLiYdevWDRvF3r59m/Pnz4uFRTt27Bj1RILH0dnZKUZd9fX16HS6p5bId3Z28sknnzBp0iTefvvt5z6H69evk5GRwbp1616a88vLwihko2AsCVl2djY6nW6IMNna2hqkhVQqlUEEJZPJ6O7uRiaTUV5ezq1bt+js7MTZ2Zng4GDi4uLw9fXFxsaGnJwclEol69evJyYmBgcHhzEhVI+jr6+PU6dOcenSJaRSKdbW1shkMjo7O3FxcWHRokUsXryYqKiop6ZgtFotHR0dtLW10dbWJvahmZmZUV9fT1RUFJs2bcLCwgKVSsXf/u3fUlVVxU9+8hOKi4vFNONIrZlkMhnnzp0jLS2NmpoarKysmDRpErNmzcLHx0ccj9Pc3ExPTw/wXWXko+I2XGN4QUEBp0+fJiAggHXr1j1VYO/cucPFixdZsGABM2bMGNH5vyyam5spKysT2yu0Wu0ThU2j0bBv3z46Ojr44IMPxGpRfT/Y1atX6e3tNSj91+l0nDp1isLCQtasWTPsRby6upp9+/ahVCpxcHB47nEsOp2OpqYmUbykUumoSuQFQeCrr75CJpPx8ccfP3dK8cGDB3z55ZcEBQXh5OTEhAkTXvvffjQYhWwUjCUhg+/ezEql0kCcHr0plUrxeDMzM5ydnbG3t6egoICcnBxsbGxYuHAhK1euJDw8HFNTU3p7e9m/fz9yuZwtW7a8UCfzF40gCFRXV3P8+HGuX79Ob2+vePHq6OjAxcWFt956i3Xr1uHu7j7s78tkMlGw9KLV0dGBVqsFwMHBAU9PT9zd3amsrKSqqorGxkaSk5P567/+aywsLJDL5fzkJz9BKpXy0UcfPZOY6c+nqqqKs2fPkpWVRUdHB66uriQmJjJnzhzR1V8ikRjcurq6gO8KbLy9vcX9Nh8fH9zd3WlsbOTw4cNYW1uzadOmYV+LwaSnp5OZmTmmRqcMDAzQ2NhITU0NtbW1NDU1DStstra2fPHFF1hbW7Nz507a2trEYpLIyEgWLFggpgP1vWAFBQW88847xMTEDHlcmUzGp59+SmNjI+7u7mzZsuWZ3EnUajXV1dWUlZVRXl5OX1/fM5fIZ2dnc/78ed59990hFZUjQavV0tTURE1NDQUFBZw5cwZnZ2emTJlCSEgIcXFxBtPcxzpvjJDpLyCzZs3CxsbmtbgzjyUh279/P42NjahUKvF75ubmjy2k0AtYaWkpv/vd76ipqWHatGm8//77BlVZMpmMffv2MTAwwLZt216J68NoEQSBpqYm8vPzuXTpEiUlJajVary9vbG0tKSrqwsHBwcWLVrEunXrxL2vvr4+UagGC5ferd7a2hpPT088PT3x8vIS/60XIkEQ0Ol0lJWVceLECdLT0wkLC+MnP/kJ0dHRtLS08P/8P/8P/f39rF27lqqqKtH15FlMc5VKJTk5OVy4cIHS0lKUSiU+Pj5MnTqVKVOmEBkZKUZX/f39Q8Sto6MD+G4f1MvLCwcHB+7fv4+ZmRk7dux44lgRQRA4d+4cubm5rFu3jsjIyFGf/8tmYGCAhoYGamtrDYTNzs4OJycn7t69i7m5Od7e3vj4+Awp7xcEgbNnz5KXl8eqVauIjY0d9jG++OIL8vLy8PLyYvPmzaN6LeRyOeXl5ZSVlVFdXY1Go8HDw0NMGT5LiXxXVxeffPIJsbGxLF26dES/o684rqmpoaamhvr6etRqNSYmJlRVVeHt7c1f/dVfERAQ8EZVK+oZ80LW0dHB+vXrSU9Px8TEhIqKCkJDQ9m1axfOzs78/ve/f2XnMpaE7NatWwiCYCBatra2j30TdnV18eWXX3LhwgVcXFz467/+a2bNmmVwfHt7O/v27cPMzIxt27a91lEZjyIIAs3NzRQXF1NYWEhpaSl1dXXodDrc3Nzw9PRELpdjaWlJUlISSUlJaLVaA+HSNyubm5vj4eEhCpVeuBwcHAxSVG1tbbS0tIjC0NrairW1tVjBWFdXxzfffIOFhQWzZs1i5syZCILAn/70J0xNTZk9ezZNTU3PJWb6597U1ERWVhbXr1+npaUFMzMzfH19mTx5MjExMURGRg65f5VKRUtLC83NzeJzaGlpoaioiO7ubpKTk5k6daoYuXl5eRkU4+h0OrH4YbheqrGGXtgqKipIS0vjxo0btLW1ERcXx/r168XJBfpo9Pz589y7d48VK1YMa3MlCALHjx8X56Bt2bLlqXZYgiDQ1tYmpgybmpowNTUlMDBQTBk+jwuOIAjs27ePzs5OPv7448dWderPQy9cdXV1KJVKLCwsCAoKIjg4mMDAQK5cuUJnZ6dBGvZNZMwL2bZt28Q+kcjISAoKCggNDeXy5cv89Kc/pbi4+JWdy1gSspGiUChIS0vj66+/pquri0WLFvHxxx8Puei1tLSwf/9+bG1t2bZt25iwsBksXiUlJXR2dtLR0UFzczNyuRxXV1d8fHxQq9V0dHRgZ2eHn5+feDE2MTHB1dV1SITl6uo6ZB+xtbVVvNBLJBJxwrWJiQkeHh54e3vj7e2NQqGgoaGBpqYm0XC4rKwMe3t7vL29CQgIwMbGhuLiYmxtbYmOjhZTnM8jZnqUSiUPHjzg1q1blJSU0Nvbi7W1Nb6+vkyYMIHo6GgmTJjw2MdRq9VIJBKOHz9OdnY2Hh4euLu7IwgCpqameHp6Guy5eXh4cPToURoaGti+ffuYTzMXFBSQlpZGf38/U6ZMoaOjg8uXL4upQJ1Oh62tLTKZDKlUyoYNG5g7d+6wC8Bbt26JKcpt27Yxbdq0YR9Xq9UauMjLZDKsrKwIDw8nIiKC8PDwF9Zjdu/ePc6ePcu2bdsMMimCINDR0SGmXWtqalAoFJiZmREQEEBISAghISH4+flhZmZmYPH1uMKYN4kxL2Te3t5cunSJuLg4HBwcRCGrqalh4sSJYlXXq+BNEjK1Ws3du3c5e/YsxcXF+Pn58aMf/WjYFWV9fT0HDx7E1dWVLVu2vPTGzieh77sqLi6muLhYvCjIZDIKCwtpaWkRoyK9kJiZmRETE0NCQgLe3t6icLm7uw/r/j04ypJIJHR2diIIAmZmZnh5eeHj4yOmo7y8vIYdSqjVasXyfH3Zt42NDZaWlvT09NDT04OtrS0RERGMGzeOvr4+3NzcXoiY6V+npqYm7t27R15eHq2trZiammJpaYmbmxthYWHExMQQERExrNuJIAhkZ2dz8eJFwsLCmDFjBh0dHWJBSVtbG1qtFhMTE1xcXHj48CEmJia89957REVFvZZZYE+irq6OS5cu0dzcTExMDPPnz8fZ2RlBEPj2228pLi5m06ZN6HQ6jh07xq1bt0SxtrOzM9hjc3d3p7q6mt/85jf09/ezY8cO5syZY/B4/f39oot8RUUFKpUKJycngxL5F91qIpPJ+Mtf/sLEiRNZtmwZMplMjLhqamqQy+WYmpri5+cnCpe/v/+w71992f7LNl1+VYx5IXNwcCAvL49x48YZCFlOTg6LFy8W9wFeBW+CkOl0Ou7fv09GRgalpaWo1WqmT5/Opk2bhvWiq6qq4vDhw/j5+bFx48bXcoHSi1dJSQl5eXlitGNtbU1dXR2lpaX09fXh5OREbGwsQUFBtLW1YWJiQnJyMsuWLRvSyyMIAnK53CDKkkgkdHd3A/+nKEJ/MfP29sbDw+OZqzLz8/M5fPgwQUFBuLu7k5GRwdWrV9FqtTg5ORESEiJ+/au/+qsXmrbVR2n37t2joaEBpVKJtbU1JiYm2NjYEB4eTnR0NBEREUP+vhUVFRw7dgwXFxc2btwoppf0aVn961ZXV8eFCxdQq9UkJCTg6+trUFDi4+Pz0mfEDUdXVxdXrlyhpKQEPz8/Fi9ePCS60Gg0fPXVV3R2djJ+/Hju37/PW2+9xaRJk4bssemj8OzsbPr7+3nvvffEuWf66Lu8vFxMa/v6+ori5eXl9dL2lwRB4LPPPqOyspKpU6fS2NiITCbDxMRE7EHUpwuf9hmuqqri66+/Ztq0aU+cV/YmMeaF7O233yYhIYF/+qd/wsHBgQcPHohD9fSrq1fFWBYyvc1OWloaEomEnp4e7O3tWbRoEampqcNuKJeWlnLs2DHCwsJYu3btqMehPw9KpZLi4mKys7PJz8+ntbWVgYEB7O3tMTc3F30LBUEgMjJSbAHIysqivr6e8ePHs3DhQjEt1tXVZbAPJJFIxD0xW1tbA9Hy8fHB1dX1hV90cnNzOXPmjDgn6+HDh/zDP/yDODrGzMwMOzs7cXJ2WFiYOE/tRZyPvtctNzeXoqIiFAoFtra2mJub09/fj4WFhShq48ePFy94bW1tHDx4EI1Gw8aNGx87o0wmk/GnP/2Jvr4+pk6dSldXFy0tLWKxjIuLCz4+PgYC97Kie5VKRWZmJnfu3MHOzo758+czceLEx76Gcrmcn/3sZ9TX1/Ozn/1s2OnHarWaqqoq/vVf/5W8vDwiIiLw9/cXZ9KZmpri5uYmpm/Hjx//0hw94LviJH2aMDMzk+zsbGJjY5kwYYIYcQUFBY0qwu/o6ODzzz/H39+fTZs2DXtd0Ol0DAwMjLmo+0mMeSErKSlh9uzZTJ48mfT0dJYvX05xcTGdnZ3iaJFXxVgVstraWq5cuSK6tPf09ODq6srq1asf2+2fn59vMDvpZfWHabVa2tvbxaKL8vJyioqKqKmpES+uISEhBAQEYGZmRlVVFU1NTVhaWhIfH8/mzZvx8fEhPT2d/Px83N3dSUxMxMrKykC49NWbjo6OBqlBHx8fHB0dX1kl1mBH9Llz5/LgwQOOHDmCm5sbGRkZdHR0YGNjQ0BAAHFxcXR3dyMIAra2tgaDQn19fZ9rYdHf3y9GaVKpFBsbG5ycnNBoNOJYj3HjxomiplarOXz4MC0tLaxevfqxDbEdHR0GM+gsLCzo7Ow0KCiRSCTi38PJyclgAeHr62vgoDJa9BmH9PR01Go1KSkpJCcnP/W1unbtGmfPnkUulzNv3jzWrl075D2hL9Q5fPgwkyZNws/Pj9bWVpRKpeg9qK8EHpyKfNahro+iVCqpq6sTU4Wtra3Adwux4uJiEhMT2bFjxzP7cyqVSr744gsA3nvvPTGCfrQwpLa2lqSkJKNF1YumpaWFTz75hNzcXHQ6HQkJCfz4xz8ekSHqi2SsCVlraytXr16loqICLy8vLC0taWhoIDIykuXLlz92paa/2E6ePJm33377hfjqDe7HGlzeLpVKkcvltLW1iaNPXF1dmThxIrGxsZiamlJSUkJ5eTkSiQRLS0smTZrE8uXL8fX15eLFi6SlpaFQKPD19cXKygqdTgcgFnsMFq4XZcKrT012dXWJt5aWFiwsLHBychrWNcXW1hYzMzOysrK4cuUKc+fOZdasWVy8eJHs7GxWrFjBoUOHyMvLo7+/n3HjxvHTn/4UGxsb0QarsbFRdF338fExELdnWf0LgkBDQwO5ubmiWXJAQAB2dnZ0dXXR3NyMubm52MtUWlrKw4cPmTt3LjNnzhz2Ai2RSNi7dy9+fn5s2rRpyF7Q4Ah5sMD19/cD320XPNrIPZLFRnV1NZcuXaK1tZW4uDjmzZs3otfkxo0bpKWlMW/ePDw8PDh8+DCpqanivpe+RP7AgQN8++23hISEsHz5cqKiosSozNTUFLVaTX19vZiKbG5uRqfTYW9v/0zCpr8/vYBIJBIEQcDJyUmssAwODub06dNIpVI+/vjjZ07f6nQ6Dh48SGNjI++99x6mpqYG+2t9fX1iYUhoaCjjx483WlSNRT755BM++eQTamtrAYiOjuYf/uEfWLJkyYjvY6wImU6n4/Tp0xQUFODq6kpcXByFhYV0dXWxePFiJk+e/FgLnxs3bpCeni6mv55lJdnb2zukgVgqlYopJisrK2xsbFAoFHR2dqLVanFzc2PixIlERkai0+l48OABZWVlyOVy+vv70Wq1eHt7M378eCwtLbl//z737t1DqVQSEBDA5MmTCQwMNCgVf949GbVabSBUg28ymUycedbd3S26fHh6epKQkIBSqTTo4dNjY2ODnZ0djY2NVFVVMXXqVBISErh+/Tp9fX1s2rSJGzducP/+fRobG7G2tmbp0qXMnTuXcePGiavjwR6P+mZnJycnsfQ/ICAAb2/vUUXS/f39FBQUkJubi1QqxdXVlXHjxmFhYUF1dbUoavr9sdTUVFavXj1s0UJtbS1ff/0148ePZ82aNU9dDOlfR72o6QVOn/61s7MbIm7Ozs6YmJiIlYdlZWUEBgayaNGix6Y/H0W/qJgzZw6pqakAZGZmcvr0aWJiYhgYGKCpqYmmpiZu3bpFbGwsv//970dUnalSqQz22B4VNv2+lT5trNFoaGhoEKOexsZGdDodDg4O4vEhISHi84bvrjmnT59m8+bNjBs3bkTPeThOnjzJlStXiI2NFSehm5iY4OvrS2hoqJgVsbCwoKmpCWtr65c6LPdFM+aFTD8b6HHMmjVrRPdz5swZzMzMxCbQr776it/+9rfcv39fdEt4GmNFyAD++Mc/4uHhgaurKzk5Obi5ubFmzRo8PT2HPV4QBK5cucKtW7eeuNoejEqlMhAs/e3RfiwvLy+xUEIqlVJTU0NnZyfW1tZERkYSHR2No6MjhYWF5Ofni64ZnZ2dtLe3Y2ZmhqenJx4eHvT399Pa2oparSYmJoYVK1YQERHxTGk2nU43JKoafNM/D0B0wNff7OzskEgk3Lt3j7q6OlQqFY6OjjQ1NTF58mR+/vOfY2dnh0KhGNakWS6Xk5OTQ1FREWFhYTg4OHDv3j0sLS2JjY2lrKxMTImamZmJ5f2TJ08mKioKBwcH0RtTEAQ6OztpamqioaGB5ubmIc78+ttI9qT0Udq9e/coKSkR9yHDw8ORy+WUlJRQUFBARUUF48aNY9euXcTGxg75G5SVlfHNN9+IPo7PMj5FX5AzWODkcjnw3aLAw8ODxsZGHB0dWbBgAVFRUSN+nNu3b3Pp0iVSU1OZNWuWWCL/8OFD7ty5Q2dnJ++88w7+/v7853/+Jz4+Pvznf/7nM0f1jwpbY2Mj3d3d4t9Yo9Hg4OAgDnjV3x4XwXV3d/OXv/yFqKgoVqxYMapz6e/vN9hfu3PnDuHh4UyePNlgf23wYlAmk3Hq1Clu3LjB9OnT2bVr1zO9Dq+DMS9kw630Bv/R9TZCz4Krqyu//e1vR/wHG0tC9sknn5CWlkZ7ezshISGkpqYSFBSEv78/fn5+Bhc0nU4nujQsWbKEqVOnGtzXo/tYesGSyWTAd6+3vul4cE+Ws7MzHR0dYql8e3s71tbWYj+Tn58f9+7d4+bNm5SXl4uzx7q7u+nu7sbBwYEpU6aQlJSEk5MT5eXl1NTUiEMWH50DNRwqleqJUdXg94ejo6OBWD0qXCYmJkgkEs6ePUtmZiYtLS04OTkRGRnJzJkziYyM5MaNG+zZswcPDw+2bNnCzJkzH9tzJwgCly9f5vbt2yxbtgxnZ2c+//xzQkNDmTp1KpcvXyYnJ4fe3l4cHBywsbGhpaUFc3NzfH198fb2Ft//+gpE/eicgYEB+vr66OnpEaNHS0tLvLy8CA0NJTQ0lICAgMca7OpRKBRilNbe3o6bmxuTJ08mICCA7OxsDhw4gEKhYPLkySQkJBAdHU14eLgYpeXn5/Ptt98yc+bMF7an0tvba7D/6evry7Rp00ZVzn737l1Onz5NcHAwzs7OVFVViaN2IiIiCAsL49q1a7S2tpKXl4e5uTmffPLJcznZPOqeUV1dTXt7O729vWLk7OTkhLe3txitDY7YBiMIAgcOHKC1tZUf//jHT80+DE5TVldXi4VSJiYmlJeXM23aNLZv3z7kvdrf309ZWRlnz57l1q1baDQawsLCWLdu3ZCWg7HMmBcyfbm0noGBAe7fv8/f//3f87/+1/96pg+PVqvl6NGjvPvuu9y/f3/ETs9jRch0Oh1/+ctfkMlkYuGDPj2iUCiA70Taz88PHx8fCgsLaWpqYtWqVWLp+qO+gvp9J0dHR4PmYX2kNPgi0tbWRklJCcXFxaI5r95ux9LSkuLiYnJycnj48CFKpVKsZtPbR3l6erJo0SJmz56NIAjcunXLYOx9QkKCeAF/XFTV2dlJV1eX+Hzhu5L6xwmVs7PzYy+EKpWKK1eucOnSJUpLSzE1NSUiIoL58+eTlJSEt7e3wYXm3r17/PnPf8bExISQkBASExNJSUkZtohBEATRQWLVqlXiKJC3336bKVOmkJaWxsWLF+np6RFd+XNzcyksLMTc3JyJEycSHh4uCtfjRvQolUq6u7vp6emhu7tbLKSxs7PD29sbPz8/AgICCA4OFoVbf7OxscHExIT6+noxSgOIjIxk/PjxXLhwgaqqKvz9/dHpdFhZWREREUF0dDRhYWHk5ORw6dIlFi5cSHJy8nO+u5+Prq4uTp48yenTp3F0dBSbgIcrkZdKpSxdupS+vj6OHj06ahuukbhnhISE4OPjg6mpKSqVasgemyAIYmpxsLDpi7E2bdo0ZJwMfHcN0/tOVldXi/ZcDg4OYsTl6urKkSNHcHd3Z9u2bZiZmYlmxZWVlVRUVJCbm0tNTQ2WlpbMmDGDZcuWien9N4kxL2SPIzMzk5/+9Kfk5uaO+HcKCwuZPn06SqUSe3t7Dh48yFtvvfXY4/UTjPXk5+eTmpr62oUMvpsd5e3tbbDZrd9kb2xspLKykpKSEtLT05FKpXh4eODo6IidnR2Ojo54eHgQHByMl5eXgXA9buUnlUrFyEs/dNLd3V1c4TU2NtLQ0IBEIkGn0+Ht7U18fDzx8fG0t7dTXFyMtbU1KSkpJCYmYm5uLrqRy2QyIiMjmTBhAgqF4rFRlYmJyROjqidZdD3KwMAA+fn5nD9/nrt376JQKPD392fu3LksXrwYHx+fJ95XdnY2p0+fxtPTE4VCgVarJTExkRkzZgxJTenHi+Tn57NmzRrq6+vJyclh+/btBAYGcvv2bY4fP057eztTp05l+/btKBQKsrKyKCgowMLCgqSkJKZOnTps2mu4galdXV3U1dWJLiQtLS309fWhVquxtbXF0dERJycnHB0dxUhPL2zm5ua0trZSX1+PQqHA1dUVpVKJubk5c+bMwc7OjuLiYtra2rCysmLChAn09PRQVVXF6tWrX2mDrb4xXO+qkZ+fT0VFBYmJiaxdu5aIiIhhC0IGBgb4xS9+wa1bt4iKimL+/PmsWbPmqWNxOjo6xJTd09wznsbjhM3S0pKSkhImTZrEzp07cXFxQRAEMdqrrq6mvr6egYEBbGxsRMEMDQ0V05RqtZrdu3ejVCpZs2YNLS0tVFVVUV1djUqlEj9npqampKSksHz5cqNF1eugtLSUxMTEUTl76MNvmUzG8ePH+eKLL7h+/fpjI7Jf/vKX/OpXvxry/bEgZHoe3cfSpwZ7enooKiqip6eHxMRE3Nzc0Gg0KJVKBgYGsLS0xNbWFj8/PzEd+WhKUiqV8uDBA+7cuSOuNu3s7LCyshIvDhqNRowUnJycSExMZNq0aXh6enLr1i2ys7MxNTUlNjaWsLAwFAoF5eXlXL9+ncbGRhwcHERbJ/guqnJ1dR1WqJ53MKdaraasrIzr169z8+ZNWltbcXR0ZPr06SxbtmzIXqkgCPT19YmTBHp6evD19RVbGvTzm+bPn8/AwAB37txBp9MxdepUkpOTh6R39fOu3nnnHbKzs8VRI46OjhQUFHDgwAEaGxtJSUlhx44d2NjY0NPTw+3bt8nNzUUQBBISEkhOTh71RUcQBNrb28X0U1VVFS0tLQwMDIhTERwcHLCzsxPnrPX29opejfpeOI1GQ2RkpDjio6urSyxRb29vRxAE1q9fz6JFi3BychIbs18kAwMDBi7yvb292NraYmFhQVVVFQsWLHjiYFCtVssf/vAHLl68yEcffUR0dDRHjhxh9uzZzJ492+DY53HPGC0qlYq6ujr27NlDRUUFAQEB9Pb2olQqEQQBOzs73N3dmTBhgpg+9vLyGrL9olQq+a//+i/u37/PuHHj0Gg0mJqa4u/vj5ubG/X19bS3txMUFDSqwpmxzJgXsgcPHhj8X+8C8a//+q8MDAyQlZX1zPc9f/58wsLC+PTTT4f9+ViOyE6cOCGKMvyffSwvLy8cHR25c+cOGo2GXbt2DeklUygUNDc309jYSFNTE42NjfT396PRaFCpVHR3d4tiCIgFHePHj8ff3x9LS0va29vFUnF/f3+ioqLw8fGhu7ubGzducO/ePRQKBV5eXri4uGBmZoZKpaK6uhqZTIa3tzcpKSlMmDDBQKz0aa4XhVKppLy8nPz8fLKysmhoaMDMzIxx48axePFi4uPjxSqu4ea2aTQa8b4sLCwYGBggJCSEOXPmEBAQwKVLl7h79y6rV68mPDycW7ducffuXQBR0PQirW/gLysrY8WKFVy9ehUHBwd27NiBubk55eXl7NmzR5zysGvXLvF3FQoF2dnZ3L17F5VKRWxsLDNmzHiuPZ3+/n4xktaX/uuFTV/67+vri5ubG0qlktzcXM6ePUthYSFubm7Mnj1b9LpsbW2ltraW/Px8ZDKZuEDy8vLC399fFMnhhrsObl943EKlt7dXdJGvqqoSMwL6lGFnZyenTp0SW0oe9x7S6XTs2bOHw4cPs3TpUv7mb/4GExMTcVHy1ltvYW1tLQrXs7pnPAsymYyLFy9y4sQJgoODsbS0RC6Xi6+JvsJRPztQf3NychIjrqqqKjIzM6mpqWH69OmkpKQQFhaGh4cHt2/fJi8vDycnJxYsWEBkZOSQ10mj0VBYWIirqytBQUEv/Dm+LMa8kJmammJiYsKjpzlt2jR279497CTXkTJv3jwCAgLYu3fviI4fK3tkAFeuXAEQ04Lu7u6Ym5sjl8vZt28fCoWCLVu2PLbXTr/alkgkPHz4kJycHEpLS8VUnr29PR4eHvj7+xMeHk5wcDAymYy6ujra2toA8PT0xMXFBY1GQ29vL01NTdTX12NiYkJkZCSTJ0/Gx8cHBwcHqqurKSkpEavPJk2a9EL614ZDv4FdVFREdnY2tbW14qo9NDRU9MHr6ekxKAaxtbUdMv168P+trKwoLy8nIyODlpYWwsLCmD17Nrm5uTx48IANGzYwfvx4FAqFKGimpqZMmzaNadOmYWNjg1ar5ciRI1RVVTFv3jzS0tKIjY1l2bJl4j7Vp59+SmlpKbNnz+b999836AdUq9Xk5uZy+/Zt5HI5EyZMICUl5YWsqnU6Ha2trQal//qFkrOzs1j639jYyPHjx5HL5cTFxZGQkCAWiPT19fHnP/+Z/Px8PD096e/vx9TUVPS+tLW1pb+/X9zXexRra+sh4tbS0kJjYyMmJiYGLvJ6F/vCwkJOnDhBfHy8+DoOhyAInDhxgt27d5OQkMAvfvELNBqNmCo8efIkNTU1xMfHExYW9szuGSOlt7fXINpraWkhJyeH8PBw1qxZI5bE6/erlEqlmIosLS2ltLSUzs5OFAoFdnZ24kSHhoYGli9fzrJly9BoNNy5c4cbN25gYmJCamqqmNofTF9fH/fu3SM7O5u+vj6DPrs3gTEvZHV1dQb/NzU1xcPDY9Q9RD//+c9ZsmQJAQEByOVyDh8+zL/+67+Kk3BHwlgSsuHo6upi3759aLVatm3bJto3dXd3DzHK1fd+dXZ2isawERERREZGEhgYSHd3NzU1NeTl5VFcXCxGX7a2tjg7O4sfssDAQNH928TEhOnTpzNv3jycnJwQBIHCwkKuXr1KX18f06dPZ+bMmS9sNavVasWqPYlEIo53qampob29HYVCIboxBAYGEhYWhre397Ai5ezsPOINbr0d2LVr12hrayMsLAy5XE5nZydbt24VV7J9fX1kZWWRk5ODmZmZKGjm5uYcOnSI+vp6EhISuHv3LkuXLmXKlCnAd8U0ejGYPXs2H3300ZALqUaj4cGDB+LwzdDQUFJSUggJCXmhEW1PT49B1CaRSNBqtQwMDFBZWYlSqSQoKAgHBwdxrMyECRM4evQoXV1dLF26FIlEQlFREV1dXdja2hIVFSVWtSqVyscWsOi/7+LiwoQJExg3btyQ9oLi4mKOHz/OxIkTWbly5RNF7MqVK3zyySfY29uzbNkyOjo6RPcMNzc3AgICKCgowMrKih//+McvfAKEUqk02F/TLwg9PDwICQmhpKSEgYEB/vZv/3bI31utVlNXVydGXVKpFK1Wi5WVFZaWluh0OqRSKffv3ycgIIDly5ej0+moqKhgYGCApKQkUlNTh7x+bW1t3LlzhwcPHmBiYkJcXBzTpk176uDVscaYF7IXxa5du0QfQr357M9+9rMRixiMbSGTSqXs3buXgYEBUlNT6evrE8VL76igLwpob2+nr69PHLjo5OQkps3gu4ims7NTXI17e3szadIk0cuup6eHpqYmcnJyxLL6cePGMXPmTNEJQa1Wk5aWRmNjI1FRUSxYsGDURrkajcYg5fdo+q+jowOpVIpUKhVTQJaWlpiamopl1vqGZFdX1xfuJSkIAsXFxaKgSaVScdbb4Ei4t7dXFDRzc3OSk5NJSEjg2LFjSCQS/Pz8qKur49133yUwMBD4Ls305z//mVu3bpGamspf//VfDxsV6HQ6SktLuXnzpnhf+pTty7DlGhgYQCKR0NDQQGVlJefOnUMqleLr64u1tTVKpRJnZ2diY2NpaWnB0dGRXbt24eDgQEtLC8XFxRQVFSGTybCzsxNFLTAwcNQRemlpKUePHhVt1ob7ff2++OnTpzl69ChqtZrk5GRxj0lfKKHf85XL5Xz++ec4ODiwffv253rPDAwMGDh36Is5nJ2dxSbkkJAQ7O3tefDgASdOnGDDhg1MmDABQRBobW2lqqqKyspK6uvrRQPqsLAwwsLCCA0NFd8Tvb29/OUvf0GpVBIYGEh6ejr19fW4u7szadIkoqOjDVKR1dXV3Llzh8rKShwcHEhKSmLy5MmvdfLF8zAmhew//uM/Rnzs3/zN37zEMzFkLAnZ7du3aW1tpaenh7q6OjIzM9FqtYSFhYkXdH2FW1dXF1KplK6uLszMzMS9tJCQENzd3XFxccHBwYHOzk7q6+uRSqXY29sTExNDfHw8fn5+4kVRp9NRVFTEtWvX6OjoIDg4mNDQUBQKBY2NjdTW1lJRUYFUKsXf35958+YxefJk/Pz8cHFxMbi4DgwMPFakuru7xcZY+D8Vi9bW1vT09NDR0UF3dzfW1ta4uLiI5c329vZMmjSJKVOmvLLp1jqdjsLCQtLS0sjIyMDJyYlf/OIXQ9Lecrmcmzdvkpubi4WFBYmJiVRUVIj+i1qtlg8//FCMBPRpuoyMDGbNmjXsSl2PIAhUV1dz48YNamtrcXd3JyUlhYkTJ740H0347m946NAhbt26JTbYFhUV0dzcLKYQw8LC+NnPfkZ0dDSWlpYGc+aKi4vp7u7G3t5eFLWAgICnilpZWRlHjhwhMjKS1atXi8fr3TP0kY8+miwpKcHCwoItW7bwzjvvGLhnPEpzczN79uwhIiKCd955Z8QLAq1WS1NTkyhcDQ0NYpp+cAP0owu63t5e/vznP+Pn58fEiRPF6sLe3l4sLCwIDg4mPDycsLCwYZun9e7+TU1NBAUFib2Ys2fPxsTExMDdX99uozeHWLJkyah79MYiY1LIRjqJ1sTEhOrq6pd8Nv+HsSRkK1asoKWlhd7eXrq6urCwsMDNzQ0LCwtMTU1RKpUolUq0Wq1YCajvJdILl5WVlbi3pS+b9/HxYcKECeIwQEtLSywsLLCwsKCuro6cnBxkMhkTJkxg7ty5YsWWvvDmxo0bqNVqxo8fj7m5OdXV1WJVmyAI2NjYYGVlhZmZGebm5uKK19TUFEdHR4NUnz79Z2pqSkNDA2VlZTQ0NGBqaiqOZNc3nHp5eZGUlMTEiRNfWw+MTqfjzp07/P73v6evr4/NmzezZMmSIWmanp4eUdBMTU3p7u7GxsZGrIbbvn27eGFRqVT813/9F+fPn2fGjBn89//+35+6X9PY2MiNGzcoKyvDyclJjABf1nQDQRC4efMmaWlpxMTEsHjxYlpaWsjOziYjI4Nr165hZmZGXFwc8fHxxMbGGvhHDh6eqm+UHyxqj164KyoqOHz4MBEREaxcuZLW1lYD8dBoNNja2hIcHIxWq+XWrVvI5XIWL17M+vXrRyRMxcXFHD16VPTLfNzzHtwAXVdXh1qtxtra2sBy6nFN6RqNhrq6Or788ksePnxIZGQkFhYW+Pj4iFFXQEDAE0VGEASOHTvG2bNn8fDwwMfHh3nz5ok+pvCdUObk5HDr1i1aWlqws7PDwcFBzMDoTZD1xSzDjXsa64xJIRurjCUh+5//83+Kexb+/v4kJycjk8loaWkRL4xhYWEEBwfj6+uLIAgMDAygVqvp6ekRS5e7urqwsrLCz88Pb29vLC0tUavV4rFqtZrOzk5qamro7e3FxcWFkJAQHBwcxHL+pqYm8UPs5uaGs7MzWq0WrVYrTqMdGBjAxMQEnU6HRqPBzMwMa2trPD09xRVnYGAgfn5+WFtb09fXR2VlpWgmrF9Buru709XVRXl5OYIgEBUVRWJi4rAXvNdFV1cX//RP/0RtbS2RkZFMmTKF1NTUITPT9BWe2dnZFBUVif19s2bNYtmyZeJxGo2G3bt3c+zYMZKSkvh//9//d0TFB21tbdy8eZOioiKsra2ZNm0aiYmJL6VwAb6bVHHy5Em8vLzYsGGD2CBeVlbGP//zP9Pe3i5Gm46Ojnh7e+Pi4iKKmr+/P1qtlocPH1JSUkJPTw+Ojo5MnTqVadOmYWZmRkVFBZ9//jl2dnaEhISIe7dWVlYGF2MvLy8qKirYv38/LS0tTJo0iffee29U+7PXrl3j2rVrrF+/nsjISIMpzPqbvvE8MDBQFC59A/SjCIKAVCoV97nq6upET85ly5aRmppKWFjYiC2yBldgRkREsGbNGpKTk8WFXGtrq7j/ZWpqSnx8PFOnThU9FPv7+w362PRuIM7OziQnJ5OUlDTi1+p1YxSyUTCWhCw7O5uTJ0+KYtDa2oqFhYU4mmPcuHEGkYlWq6WyspL79+9TXl6OqakpEyZMID4+npCQkCEfPH1Bw/nz58U8enBwMGZmZqKrRltbm1gR6OLiwrhx48TqPv3N0tISc3NzzMzMGBgYYGBgAJVKRU9PD52dnaIbRW9vL2q1WmwD0Edv3t7e+Pv7Y2ZmRmtrK729vdjb2xMeHk54eDgODg5i1DiSr49+bzT7Mvp+nJH8TkdHB1988QUymQxnZ2dUKhWTJk1i1qxZQ1a8MpmMy5cvs2/fPnp6eggKCuKv/uqvDKzEdDodBw4c4KuvviIhIYFf/epXIxakrq4usfTa1NSUKVOmMH369BdezADfpeUOHTqEqakpmzZtwsvLC4Camhr279+Pi4sLHh4eFBUV0dfXh7OzM/b29mJTud6ey9/fH3Nzc9rb27l3757YR3Xr1i0cHByIi4sz2ON6VDxqa2vZv38/bW1t+Pr68uGHHw5ZSDwNQRDYt28fOTk5JCQk0NXVJfaR+fv7i03Ifn5+j42aFAoF1dXVonj19PRgbm5OUFAQPj4+XLt2jbi4ONauXTuq86qoqODgwYNcv36d1NRU/q//6//C0dERQRCorKzk9u3bVFdX4+joKO5/Pe39MljY/P39R+xBOxZ4I4SssbGR06dPU19fLzqs6/nDH/7wys5jrAiZRqNh586dqFQqccjfcOIFiJVMBQUF9PX14ePjQ3x8PDExMeh0umH3qGpqarh//z4dHR2i5Y2+H0zflFxRUUFzczNBQUEsW7Zs2J6Up6HvCXzw4AEPHjygurqavr4+rKysxLEZes9Hc3NzAgICiIqKYvz48ZiamopR4+DocfD3RvLW1qc3hxM9QRDo6emhq6uLjo4OZDIZlpaWolGv/iKqv+g+SktLC3v27MHT05Px48dz584dlEol8fHxzJo1a4jjRENDA//4j/9IXl4ezs7O/O3f/i1LliwR71tfPv7JJ58QExPDv/zLv4xqc763t5e7d++SnZ2NRqNh0qRJzJgxY9QX+KfR09PDwYMH6ezsZO3ataJre2lpKUeOHGHy5MmkpqaSn59PXl4eXV1deHh4iNWPevd/fR9jb28vBQUFNDY2EhMTwy9/+UsmTJjw2L2/5uZmvvrqK7q7u7GysmLr1q2iWfjT0A+zrK6upqamBqlUSn5+PtbW1mzZskUsTHlc+lpvHVVZWUlVVZU4msXT01NMF+rbP44cOUJ9fT0ff/zxiKOw1tZWLl26JM70mz17Nj/60Y/QarUUFBRw584d2tvb8fX1Zfr06URFRb3UPdKxwpgXsrS0NJYvX05ISAhlZWXExMRQW1sruhykp6e/snMZK0IGkJGRITaEPvqhUiqVFBUViasyAD8/Pzw9PTExMRm22dfKygoTExOxd8jX15fU1FTi4+PFRuWBgQFu3rzJrVu3sLGxYd68ecTFxY1KwPT+dCUlJZSUlCCVSrGysmL8+PFERUURGhoq7sWVlpaiVCrx9PTEwcGBnp4esQLTzc3NwJHk0ZEmgiCg0WiGiN3jRE+tViOXy8X9QolEIjpZ6GeQOTg4oFAoxChS3wdlbm6Oq6srnp6eeHt7i03EdnZ2yOVy0tLSCAsLY8WKFVRUVJCXl4dGo2HKlCmkpKQYREY9PT38x3/8B5cuXcLS0pL58+czf/584uPjRUE7f/48f/jDHxg/fjy/+c1vRj2oUqlUcu/ePW7fvo1CoSA6OpqUlJQXOn9KrVZz/PhxysvLWbx4MUlJSZiYmHD//n1OnTolDh7VF6ncu3ePsrIyzMzMiImJYfLkydjb29PU1ERXVxfp6ekIgiAuPPS9iI++96RSKXv27EGlUqFWq1m0aBEpKSmPPU+9m4ZeuPTl+O7u7mLE5eLiwoEDB3BycjLYvwTEyQT6iKumpkZsUxlcXfjooqWoqIhjx46xdu3aEUU+crmcjIwM7t+/j729PV1dXXh7e7N+/XoKCwu5d+8e/f39TJgwgenTp4+pVPurYMwLWVJSEosXL+Yf//EfcXBwoKCgAE9PTzZv3szixYv50Y9+9MrOZSwJmU6nEw1iZTKZuG9UVFREdXU1CoUCZ2dnfHx8xIvq4xp9BwYGuH37NiUlJbi5uTFnzhyio6PFD4IgCBQUFHD16lWUSiXJycmkpKSMqu+qpaVFFK+Ojg7RaDgqKoqwsDDUarU4g6yrqwsfHx+SkpKIiYkRixT0F43BjiStra1iSkqfhtS7SjypMg2+uzjU1dWJN31fj7Ozs2j6GhQUZFBtKQgCarVabGOora0V9zqam5vp6elBrVaLe4D6kvS6ujqxkEar1dLa2iru/U2YMIFJkybh5uaGra0tOp2OU6dOkZeXh7+/P4GBgbi5uZGamsqkSZMwMzMjIyODf/mXfyEoKIg//OEPzzR0U+83mZWVhUwmE1so9C0Az4tOpyMtLY2srCwSExNZvHixweDRxYsXM23aNIO/x/3798nLy0Mmk+Hl5UVUVBRZWVniEE+VSsXly5d58OABQUFBvP322+LoIplMxu7duxkYGECpVBIVFTXEP1Ff1agXLv0MMf0wS/0+16Np16amJvbs2UNUVBRLliwRbb6qqqqQyWSi36JevJ7k1amvRg0ODmbdunVPfA31n82bN29iZmbGrFmzKC8vp7y8nIiICOrq6jAzMyMhIYGpU6eOusXl+8KYFzIHBwfy8/MJCwvDxcWFmzdvEh0dTUFBAStWrBAHZb4KxpKQ/cd//AednZ0olUpaWlro6upCp9Ph7u4uls3rL+ZOTk7DbnJ3dnZy7do1CgsLcXJyYvbs2QbVTvBdQ/rFixeRSCTExMQwf/78EVU16c1cS0tLKSkpoaurCxsbGyZMmCBGXqampmI/WnFxMQAxMTEkJibi6+s7ohWlRqMRnR/04qYfRGlra2sgbLa2trS1tYnC1dHRAXwX3Q0WrucxT+3p6aG5uZmmpiaam5tpbm4WK0NramqIiIhg2rRpODo6Ym5uTmFhIYWFhahUKjFq1vf73blzh5aWFiZMmICtrS1dXV04OjqKA0obGho4dOgQnp6e/OIXv8DX1xc7OztsbW1HZfWlb6m4efMmbW1tBAYGkpKSwrhx417Iqj4vL4+zZ88SEhLC2rVrsba25sqVK2RlZbFq1Sri4uKGnI8+SisvLycgIIDNmzcbLJyqq6s5d+4cXV1dJCcnM3nyZPbv349arRatnHbt2oW5uTnNzc2icOmrGvXFIoNL4p9ka9XU1MTVq1c5efIk7u7uBAYG4u7ubpAuHGkhydGjR6mpqeHHP/7xY1OKgiDw4MED0tLS6OvrY+rUqaSkpHDw4EHOnTtHYGAgwcHBYp/k8w6ZffSxgTcqohvzQubt7U16erpYjvvrX/+a5cuXU1BQwIwZM0ZlGvy8jBUhEwSBM2fOUF5eTltbGw4ODsTGxhIfHz+ilEJ3dzeZmZncv38fOzs7Zs2aRUJCgkFqrquri6tXr1JcXIyfnx+LFi166kpdP7CxpKSE0tJSuru7sbOzIzIykqioKIKCgsSij8LCQnJycpBIJLi4uDBlyhTi4+NfSENmX18fjY2NlJaWUlhYSHl5OVKpFKVSiY2NDQEBAWKhS3x8/EstN9ZPJGhqaiIjI4O0tDTR+svExAR3d3fc3Nzo6Oigvr4ee3t7pk2bRlxcHFKplN/+9rfU1NSwbds2XFxcyMnJobKyUhwVolAoOH/+PNbW1ixevFi8MJqamorTqm1tbbG1tRX//biv+tlVN2/epKGhAS8vL1JSUoiOjn5uO7GamhqOHDmCnZ0dmzZtwsXFhTNnzpCfn8/69euJiIgY9vcUCoXYrvEoGo2GrKws0tPTKSkpEVOBjY2NzJw5k46ODnEo6uCqxpCQEDHN/jhkMpnYjFxTU4NSqcTa2hqFQkFLSwvvvffeM1X16cv616xZQ0xMzLDH1NXVcenSJZqbm4mKimLWrFnU19dz7NgxsrOzmTFjBps2bSIyMvKF27zJ5XK+/fZbxo0bZxAtj3XGvJCtXLmSt99+m/fff5+/+7u/4+TJk2zfvp0TJ07g4uLC1atXX9m5jBUhA/j000+xsLAgPj5ebDR9Gr29vaKhr5WVFTNnzmTKlCkG/UUqlYobN25w584dbGxsmD9/PrGxsU9crdbX14viJZfLxebWqKgoA8eGjo4OcnJyyM/PF91AEhMTCQ8Pf+7Vn760eXCqUC6XY2JigpeXF66urqKVT3t7u0FK0sfHx2AKwNNSks9DZmYmaWlpTJs2DW9vbzF6a21tpb+/n6amJrHSMSUlhfj4eP70pz/R3d3N73//e8LCwmhtbeXatWuUlpbi6uqKr68vX3zxBVZWVvz85z8X9/L0o12G+zrcQFobGxtR9BQKhWj15erqytSpU4mPj8fJyUkUv9E20XZ0dHDw4EH6+/tZv349AQEBHD16lIqKCgNrr9GgUqn4/e9/T0ZGhmhplZycLKZk9cLl6+v7xIu+SqWitrZWTBd2dHSIfX36ZmR9luDo0aNUVlayc+fOUe0r9vX18Ze//IXAwEDWrVs35D3W2dnJlStXKC0txc/Pj+TkZCQSCbm5ueIUgkWLFrFjx46X8v4sLS3lzJkzmJqasnLlyhEXyIwFxryQ6TvcY2NjUSgU/Lf/9t+4efMm4eHh/Pu///srdWgeS0KmVqtHvEeln22VnZ2NmZkZycnJTJ061SAVotPpyM/PJz09HZVKxYwZMwx6Ugaj0+mora0Vxauvrw9HR0dRvAZHhTqdjvLycnJycqiqqsLW1lY0mX2efL7e4HawcCkUCkxNTcVRK0FBQQQEBAybdtFoNEgkEjEdqS8qALCzsxOLSPTi9qJSN4OnRq9evZrY2FjxfFpbW2lubqayspKsrCxKSkpEb9GHDx/i4ODA//pf/4uoqChcXV1paWnh2rVrlJWVodPpuHHjBi4uLvz+979/oqmAIAjiTKpHBe7R77W0tFBaWopEIsHS0hJ/f398fX0xNzcX3WMGR3VPivgsLS1RKpVitd7y5cuJjo7mwIEDNDc3s2PHjhEJQ09PDzU1NVRWVnLixAlaWloICAigrq4OJycnwsPDWbFiBTNmzHiseOkrZvVRV0NDAzqdDmdnZ1G4QkJChv27q9Vq9uzZg0Kh4P333x9xsc2xY8eoqqrixz/+scHv9Pf3k5mZSXZ2Nvb29kycOBGZTCa6kYwfP57i4mICAgLEAZkvErVazcWLF8nLy2PChAksX778jbOqGvNCtmPHDrZs2cLcuXNfe852LAnZSFAqldy5c4fbt28jCALTpk1j+vTpQ/pJamtruXjxIi0tLcTGxoqmv4PRarXU1NRQUlLCw4cPxWISvXgNtrGC76K/vLw8cnNz6e7uxt/fn8TERKKjo5/JDker1SKRSETRqq+vF4c9+vn5icKlHzPzLPT19YmTtvXipq9MdHd3F6sj3d3dcXd3f+bITT9os6Cg4IlptY6ODs6cOUNWVhatra08ePAAZ2dnli5dir29Pb6+vvj6+mJhYUFFRQUPHjwgMzMTNzc3/vM//3PUE4+fREtLC+np6dy/fx9BEIiIiCA0NBRBEIYVxOGc7c3NzcX9u/Lycpqbm5k0aRKJiYlkZGSgVqvZunWruNenn2WmUCgMzHb1c8+am5tRq9UsW7aM4uJiYmJieOutt8jIyCAnJwcvLy+WLl2Kv78/gDj8U28BpVAosLS0JCQkRBSvJ+2VDaa7u5vPP/8cFxcX3n333ae+p0tLS/nmm2945513mDhxIvDdezonJ4fr16+j0WgIDAwUDQacnZ2ZNm2aKPRKpZIPPvjghQtMU1OTOMlgyZIlxMfHv/br7LMw5oVs+fLlXL58GTc3NzZs2MDWrVtf6QTawbwpQqZWq8nOziYrK0t0vh5ucvHgVIa/vz+LFy8WP/TwXZSgH7/y8OFDlEolrq6uong9Wpml3yPLzs6mtLQUU1NTJk6cSGJi4mPHyTwOjUYjOoboJx2r1WrRRSEoKIigoKAnNqM+L3onh8HCJpVKRWsfc3NzUdT0Nw8PjxEZFOt0OjGttmXLliEz4waj39M8ffo06enpREdH8zd/8ze0t7fT1NQkelLqpxBkZmZiZWXF//yf/5ONGze+cDf8kQz61Gq1T0xt9vb2UlhYSH5+vjidID8/H0EQiI+PF3sJbWxsUCgUCIKAm5ubmCrUZwNWrFjBtWvXsLKyYteuXeLr3tTUxKlTpygrK8PNzQ0nJye6urowMTHB19dXLNLQN9s/C42Njezdu5fo6OgnOu8rFAr+/Oc/ExAQwPr164Hv3E4uX76MVCrFyclJLPAJDAxk2rRpoumzfuTPrl27xObyF4E+gr9+/To+Pj6sXr1adPx4ExnzQgbfbbweOXKEgwcPcuPGDSIiItiyZQubNm164gXgRTPWhUyj0ZCbm8uNGzfo7+8nISGBmTNnDinNVqlUZGZmcufOHezs7FiwYAExMTGYmJgwMDBAVVUVJSUllJWVoVKpcHd3F8XLy8tryAdWpVKJxRutra24ubmRmJhIXFzciN0n1Go1jY2NonA1Njai0WiwsrISRUvvhvA6Gzz1Y3Ha29tpb29HKpWK/+7r6wMQR8cMFjf9vwevqDUaDQcPHqSpqYnt27c/Vey7urr4zW9+w+HDh4mKiuLv//7vmTJliri3pt9vu3v3LqdOnUKhUBAXF8fy5cuZMmUKfn5++Pr6vpAxOi9q0OfDhw85fvw4bm5uzJ07l8OHD2NiYsLbb78tiqGTkxMhISHiaKArV65w69YtVq5cSWFhIRKJhA8++AAnJyfRMb6qqora2lrq6+uRSCR4eHiwfPlyFi1aNOLm45Ggd61fsGCBODX7UY4fP05lZSUff/wxvb29XLp0iYcPH6LT6bCwsMDKyoqoqCimT59uMFcuIyODzMxM1q9f/1xzFx+lq6uLEydOiEUxqampb3zT9BshZINpbGzk0KFD7N69m4qKCoOm3pfNWBUyrVZLfn4+169fRy6XM2nSJFJTU4dU4+l0Ou7fv096ejpqtZqUlBSSk5NFW5uSkhLKy8tRq9ViD09UVNRjL05SqZScnBwKCgpQq9VERESQlJQ0oplYKpWK+vp6UbiamprQ6XTY2toaCNdwo9zHKgqFQhwtoxe39vZ2urq6xJJmW1tbA3FzdHTkypUrqFQqdu3a9dQ5UIIg8Lvf/Y79+/cTFBTEnDlzDPrL9MeUlJTw05/+lMrKSry9vfHw8CAgIEAUVL2o6dOlzxrVvohBnxKJhEOHDgGwcOFCzp8/j5ubG1u3bh2SJs7MzCQ9PZ233nqL7u5url+/TnJyMiqViqqqKgPHeH3UZWVlxaVLlyguLiYkJIS33377hc7bSktL4+bNm2zYsGFImvjhw4ccPnyYhQsX0trays2bN8VqXm9vb6ZMmUJSUtKQiFZf3Thv3jxmzpz5Qs5TX9J//vx5bGxsWL169bCVyBqNBp1O99rMt5+FN0rIBgYGOHfuHF9//TXnzp3D1dWVpqamV/b4Y03I9ONDrl27RldXFzExMcyePXvYD2lNTQ0XL16ktbWVuLg4UlJSxCblyspKBgYG8PHxISoqisjIyMd+0LVaLWVlZeLkZXt7e7F440n9VwqFQhSuwQal9vb24v5WUFDQY53C32Q0Gg0dHR0G4qa/6f0nHzx4gLW1NUuXLiUoKEgUHP1Eg0fv79e//jUZGRlMnDgRFxcXXFxcSE1NNegDbG5u5le/+hVVVVVERkZiZWWFtbW1ePHSV27qJzjrhc3X1xdPT89R+1A+z6BPuVzOoUOHaG9vJyUlhZs3bxIYGMjGjRtFgc7Ozubs2bNERUXR09PD6dOnxSZ4b29vUbgCAwOHFeaKigrOnz9PT08PKSkpzJw584WkpQVB4JtvvqG6utogBdjf388f//hH5HI5crlcbDWJiooiOTmZSZMmDSsWEomE3bt3M2HCBFavXv1CPg/9/f2cPXuW4uJi4uLiWLJkyZBCFr3N1fXr14mJiRnVrMbXzRshZBkZGRw8eJDjx4+j1WpZvXo1mzdvZu7cua90tT5WhExv6JuRkYFUKmXChAnMmTNn2Bx6R0cHV65c4eHDh3h7exMaGkpHRwdVVVVoNBr8/PxE8XqS555cLic3N5fc3FzkcjlBQUEkJiYSGRk5bFqit7dXjLZqa2tF1wz9yAi9cLm6un7vhGukDE5T1tXVcfjwYRQKBVFRUeI+3KNpSn00Z2lpye9+9zsqKytZuXIl1tbWPHz4UHQAiYmJwdTUFIlEwr/8y79QW1vL/PnzsbOzo7GxEX9/f9EeS9+4rd8DFAQBCwsLvL29DSK3kfytnmfQ58DAACdPnqS0tJSoqCjxa2pqKpcuXeLEiRM4Ojri5uZGSUkJsbGxbNq0ibCwsBFXDg4MDHDjxg2ysrJwcnLi7bffJiwsbGR/sCegVqvZvXs3SqWS999/HxsbG37zm99w+vRp7O3t8fb2ZubMmcycOVP0Ch2O3t5ePvvsM+zt7dmxY8cLGb1TU1PDyZMnxcKYRy2xRrMgHquMeSHz9/eno6ODRYsWsXnzZpYtW/ZCu9hHw1gSsk8//RR7e3vmzJkzbBpHqVSSmZnJzZs36evrw8PDQ3Q90JvvRkZGPrEZWBAEamtrycnJ4eHDh5ibmxMbG0tiYuIQ0ezu7jYQrkddM/S3N3HW0auis7OT3bt34+DgwLp16+jt7R2yDzc4TalWq7l9+zY6nY7ly5czdepUHj58SENDA56ensyePZuoqChaWlr47W9/S1VVFcuXLyc5OZmsrCwaGxsJCAhgzpw5YuSkVquRSCQG7iSdnZ0AWFtbiz13enFzdHQcVqCeddCnIAikp6dz48YN3N3dkUqldHR0UFpaysSJE1m6dCm3bt3Cy8uLnTt3PvOFXiqVcu7cOWpra5k4cSKLFi0atWflo3R3d/PZZ5/R19dHeXk5t2/fZty4cWzdupX58+c/dQ9UPyBTJpPx/vvvP5Pt2KP3l56ezu3btwkKCmLVqlUGWRN9CjojI4P29vYnLojHOmNeyD777DPWrl07JjzExoqQAaJbwaPodDqysrI4evQoEokEBwcHceSEXrye9gFRKpU8ePCAnJwcpFIpHh4eYvGGlZWV6FYxWLhkMhkAnp6eBsL1MkaFfJ9paWlh7969eHl5sWXLlmHTip2dnaK43blzh7Nnz6JSqcSS+P7+fqRSKX19ffj5+TFnzhwCAwP5+uuvqaqqYuHChWzbto3m5mauXbsmThaeM2fOsMVT/f39Q2y39M70+p67wWnJR4sphhv0GR8f/8Q9mIKCAk6fPo2lpSUtLS0kJiaybt06Dhw4QFtbm1jc8Tzo94wuXbqEVqtl3rx5TJky5ZmyPPr7+t//+3+TkZEBwFtvvcWvf/3rEZ2nIAicOnWKoqIitm/fblA9/CxIpVKOHz+OVCpl7ty5JCcnG/iFlpeXk56eTmtrK+Hh4Y9dEL8pjHkhG0uMJSF7FLlcLnrB1dfXi6POJ0+eTGRk5IhWm62treTk5PDgwQM0Gg2RkZEkJiYSGBgo2v3ohUvvmuHt7S2KVmBg4AutCPuhUl9fz/79+wkNDWXdunVPjWBOnz7NuXPnsLCwYPr06YSHh9Pe3k5ZWRk5OTm0tLRgb2+Pp6cn9fX1dHd3M3nyZDZu3EhQUBDd3d3cuXMHiURCcHAwc+bMearRgFwuNxC3pqYmcTKBs7OzgbDpKyUfHfQ5depUkpKSHlvZWldXxzfffIOPjw8bN24kLS2Nu3fvsm3bthdardzf38/Vq1fJzc3F19eXZcuWjbhdRKfTkZuby/79+yksLMTFxQV7e3saGhr4+c9/PuJ9plu3bnH58mWDJvlnQRAEcnJyuHz5Mi4uLqxevVp8LoIgUFVVRUZGBk1NTYSEhIiLnDcdo5CNgrEmZD09PZSWlpKdnc3169fp6OggPDycd955h5kzZ45IVLRaLSUlJeTk5FBfX4+DgwMJCQkEBATQ0dEhljD39fWJrhmDhet1pXm/71RWVnLo0CGio6NZtWrVE/eXNBoNe/bsobCwEHt7exYvXsysWbPEnz98+JBz585RXl6OTqcTnf49PDxEf0sbGxs0Go04cTkiIoIlS5aM2GdREARkMpmBsEkkEtRqNSYmJri5uRlEbNXV1eLk4icN+lSr1aK58smTJ3nrrbde2uTihoYGzp49S1tbG0lJScydO/exrQpKpZLs7GyOHTtGSUmJKBqxsbEcPXoULy8v2tra2LhxI+PHj3/i4+qHZCYnJz9XgUVvby+nTp2ioqKCpKQkFixYIEb0tbW1pKenU19fT0BAAHPnzn2i+8ubhlHIRsFYETJBENi/fz9lZWU0NDSgUCgICQlhw4YNJCQkjNiZ4N69e+Tl5YkTnj08PBAEgcbGRpRKJWZmZvj7+4vCFRAQ8EaV5L7pFBUVcfz4cXGU0ZP+rj09PXz66ad0dHRgaWnJkiVLmD59uvhzQRCoqakhIyNDrFTV6XRER0ezaNEiTE1Nxf240tJSKisr6evrw93dnYSEBMaNG2fQDzdcNeWj6H0tB0duLS0tYqWkk5MTPT09tLa2YmtrS3JyMrNmzRpSdNTc3Mzu3buJiYlhxYoVL7U4SKvVcvfuXTIyMrC2tmbJkiUGQ2M7Ozu5c+cOly5doqKiAicnJ5YuXcrKlSsxMzPjz3/+M15eXmzcuJEjR45QW1vLrl27xFEzjyKVSvniiy8ICgpiw4YNz1y8VlZWxqlTpzA1NWXFihXiMNOGhgYyMjKorq7Gx8eHuXPnvhBv07GGUchGwVgRMp1Ox549eygqKsLFxYU5c+Ywffr0p5YS6zfg79y5Q25uLn19fTg4OGBlZYWFhQUWFhYEBASIwvW4ycdGXh337t3j7NmzzJ49m9mzZz/x2Lq6Ovbu3YulpSUqlWrY6EWfXvr22285f/48KpWKpKQk/uqv/kqs3tNXU96+fZu0tDSam5txcHAw6OkzMTHBycnJQNz0tydlAvSz2Abvtw0WOisrK2JiYpg3bx6xsbFYW1vz+eefi1V8r+r92N3dzYULF3j48CFhYWHExcVRWlrK3bt3qa+vF6dGLF++XOyzPHXqFCUlJXz88cc4OTmhUqnYvXs3arWa999/f4i9VH9/P59//jlmZma89957z9SorlaruXz5Mvfu3SMiIoLly5djZ2dHc3MzGRkZVFRU4Onpydy5c4mIiPjeCZgeo5CNgrEiZIIgsHfvXlxdXZk7d+5TCyp6enq4cuUK169fp76+Hp1Oh7e3NwEBAYSGhorC5evr+8Z3+H8fuXnzJlevXh0yiHI4srOzOXfuHN7e3rS2trJ8+fJh36uCIHDz5k1+97vf0dDQQEBAAH/913/N/PnzDY7T6XSUlJRw7do12tvbCQkJITo6GjMzMwN3k+Gavh9tGXBycho24lCr1bS0tFBXV8etW7e4e/cunZ2duLq6EhwcjI+PDx9++OFzV/GNFq1WK/attrS04ODggI+PD5MmTeKtt94iNDRUPLayspKvv/56yOstk8n4/PPPcXd3NzD81el0fP3110gkEt5///0ntr48jubmZo4fP05PTw+LFi1i8uTJtLW1iZMR3N3dmT17tsGQ3O8rRiEbBWNFyOC7D9njREelUtHQ0CDaVT18+BCtVoufnx/Tpk0jISGBkJCQN8o144eMIAhcvXr1sYMoHz321KlTFBYWEhwcTHV1NatWrXpsAUFzczO//e1vuXfvHiYmJixYsIBdu3bh6+trcJx++KZ+L3bChAnMnj1bdKvXV1MOZ9012JvSzc1tiHXXo2lKnU7HvXv3uHDhAvX19YSGhhITEyP2H77sFo7+/n5yc3PJzs6ms7PTwDUkPj6eDz/80EDElEolf/nLX/Dw8GDLli1DRKO+vp6vvvqKuLg4li1bhomJCRcuXCAnJ4etW7eOeq9KX5mckZGBt7e32DR97do1ioqKcHZ2HtIc/33HKGSjYCwJ2WD6+/upr6+ntrZW3ETXO7f7+PiQnJzMwoULCQ4O/t6vzL6v6Iep5ufns27duid672k0Gnbv3k1vby9+fn48fPiQNWvWDGmE1SORSNi7dy/FxcVIpVJ8fHxYuHAhc+fOHTJWRd88e/36dTo7O8WG5cf1HgmCQE9PzxBxa29vF4fi6tOUj/pSurm5IZFIqKiooK6ujtbWVuC7qsjBE71H6lj/NDo6Orhz5w75+flotVrs7Ozo7u7GysqK5ORkwsPDuXz5Mg0NDUyaNIkFCxZgZ2fH6dOnKS4uFlOKw5Gfn8+3337L4sWLsbCw4MyZM7z99tskJiaO6hxlMplYmZySkkJcXBw3b96koKAABweHIXZlPxSMQjYKxpKQlZaWUlNTI1agKRQKuru7USgUWFtbEx8fz5w5c57oImDkzUKn03Hs2DHKy8vZvHnzE1fy+uZcd3d37O3tKS0tfeLIGIlEwldffUVHRwcymQxTU1O8vb2Jjo5m9uzZQ4oVtFotDx484Pr168hkMvG40ZgG9/f3D2vb1dnZaZCmtLW1xcLCAp1OR3d3N52dnXR1ddHT04OpqanonKGvinR3d8fCwgJzc3OD23DfMzc3p7GxkdzcXCoqKrCzs8PX15e2tja6u7uJi4tj7ty5okAJgsD9+/e5cuUKABMmTCAvL4/ly5czefLkJz7fy5cvc+HCBXQ6HQsWLGDp0qUjfq3gO4Pic+fOYW1tzYIFC6itrSUvLw9bW1tmzpzJ5MmTf7B72kYhGwVjScg++eQTsSm6q6sLuVyOk5MT8fHxTJky5Y0eyWDk8Wg0Gg4dOkRDQwPbt28fkgIcTG1tLfv27SMxMRG5XE5ZWRkbN2587ORfiUTCvn37kMvlCIKAk5MTZmZm9Pb2ikL1qG2R3rA6MzOTnp4eYmJiSE1NfS57o0fTlP39/Wg0GvE2MDCARqOhv7+ftrY2WltbxX06rVaLhYUFDg4OODo64uzsjK2t7ZCITafT0dbWRmNjI729vdjZ2eHu7k5fXx8ymQx3d3eio6Nxc3MbVgw1Gg35+flcvXoVT09P3nvvPby8vJ4onHK5nL//+79Hq9Xy+9//XhxBZG5u/sTFplKp5Ny5cxQWFjJu3Djs7e0pLCzE0tKSGTNmkJSU9EKsrN5kjEI2CsaKkAmCwPXr1ykoKKCrqwsfHx+SkpKIiYn5wb+hfwio1Wr27dtHZ2cnO3bseGIUdPfuXS5cuMDKlSspLS2lqqrqidGcXsxUKhVarZaQkBAiIiK4c+cOPT09xMbGkpqaOqQ4QavVcv/+fTIzM5HL5UycOJHU1NRXuqDS7w3X1taKM+y0Wi1WVlbixG9nZ2caGhrIy8tDLpcTEBBAWFgYjY2NFBcXY2dnR0JCAn5+fmi1WgPhfFRMc3NzKSsrw87ODpVKhb+/PwEBAQA8elnVarXk5eUxMDCAqakpJiYmJCQkiJ9XMzOzYSNImUwmTpdwdXUV3f2jo6OJjo7GxsbmidHm0yJTMzOz78V2g1HIRsFYETKA/fv3Y29vT1JSEr6+vt+LN6ORkdPf38+ePXtQKpXs3LnzsQUQgiDw7bffUlJSwrZt27h27Rr19fVs3br1sY4OejETBAG1Wo23tzfr16+nrKyMGzdu0NfXR1xcHLNmzRpiHafRaMjLyxOPi42NHbY37FWgn3FXW1tLUVER2dnZNDc3i56hs2bNQqvVUlpaipmZGampqSQmJo5of6m6upp9+/bx9ttvk5CQwK1bt7h+/Tp2dnYsWbKEcePGGYje0aNHqa6uZv369ZiYmHDgwAGcnZ156623EARhiEiqVCry8vJEETMzM8PMzIyQkBCCg4MxNTV9rMiOluEELj4+nuTk5Gd52V8LRiEbBWNJyARBMIrXDxy5XM7u3bsxNTVl586dj+3fGhgYYPfu3fT397Njxw5OnjyJRCJh27Ztj/XX04uZmZkZarUaR0dHtmzZgp2d3bDDWx8tchg85FU/4HM44XuZ6JvAb9++TUVFBTY2NgQHB+Pg4EBeXh63bt2iv7+f4OBgZs2axfjx40c0vFWlUvHJJ5/g4uLCtm3bxM9hV1cX586do7KykgkTJrBkyRKcnJxEE+TB88rq6urYt28f8fHxvP322waf5fb2dg4fPkxeXh6Wlpb4+fkxderUYae8D/ectVrtY6PIR7/3uO8HBQW90GGeLxujkI2CsSRkRozA/3HMt///2rvvsKiu/H/g7wEHGDoIIihFAQsqKiggSLGEogSNkVhRozFrktVN2WySTUFNNtmY7Ca//aZqjIVYiAX1awFBwQoiKoIoKIiAFKnSYYaZ8/sjX+46UmQod2bg83qeeR7ncmfOZw7Cm3vvuefo62PVqlUdThn2+PFjbN26FUOHDuUm3y0rK8PKlSs7nFewNcx0dHS4U2LLly/HkCFDIBaLcfXqVVy6dAnNzc1wdXWFt7d3m3saJRIJUlJScPHiRTQ2NmLy5Mnw9vbu0yH0LS0tSE9PR1JSEh49eoShQ4fCw8MD48ePR1FREWJiYlBYWAhHR0eMHz+eW7mhoKAAYrEYWlpasLa25kZFWllZyQ2iOHHiBG7evInXXnutTTC3LrF06tQpNDU1wcbGBvfu3YO/vz+mT58ut+/169dx7NgxBAUFwd3dHYwxJCUlYefOnXj06BEcHR0xc+ZMbrkd0jEKMgVQkBFV9OjRI+zYsaPDGfNb5ebmIiIiAh4eHvDx8cHu3bvx+PFjrFq1qsMplFrDTE9PDzKZDI2NjVi6dCl3Lai5uRnJycm4fPkyJBIJpk6dCi8vrzaTVD8dfK1Hcr15k3N9fT1SUlJw9epV1NXVYdSoUZg2bRrs7Ozw+PFjxMbG4vbt27C0tERAQECbiYelUimKi4u5a2z5+flobm7GoEGDuBlvNDU1ERsbi+Dg4E7nfGxubsbBgwexfft2ODg44KOPPmr3VG50dDSuXLmCkJAQHD9+HBcuXIC5uTkWLlwoN1qSdI6CTAEUZERVFRQUYPfu3bCzs8PixYs7PDWWmJiImJgYLFy4EPb29ti1axfq6uqwatWqDkcatg7Nbx3FWFZWhpdeeombzw/4Y2RdUlIStz6am5sbvLy82kzLJBaLkZycjEuXLkEsFnd4JKeI0tJSJCUlIS0tDQKBAJMmTYK7uzvMzMy4dfmuXLkCPT09buqrrpyWl8lk3IwjrfdoXrhwAbq6uggODoadnR3s7OwwfPjwNnOQ1tbWYtu2bZDJZNDT08OjR4/g6uqK2bNny832L5FI8Ne//hWnT5+GhYUFQkNDsWTJEqVcU1RnFGQKoCAjqiwnJwd79+6Fk5MTN9vD0xhj3CrMa9asgYGBAXbu3Immpia8/PLLHf4CLSoqwu7du2FiYgIdHR3k5eVh/vz5bWYMaWxsRGJiIpKSkgAAHh4emDZtWpulWpqbm3HlyhUkJiZCIpFgypQpmD59epcXt2ydMzIpKQnZ2dkwMDCAm5sbXF1doaurC6lUimvXriEhIQESiQTTp0+Hp6dnj0b1njhxAhcuXMDMmTO59fgaGhqgoaGBYcOGcTdpW1paYt++faiursarr77KXVeMi4uDpqYmAgICMH78eNy4cQNbt25FZmYmNDQ04Ofnh/fee6/DZW1IxyjIFEBBRlTd7du3ceDAAUyZMgVz5sxpN8wkEgm2b9+O5uZmrF27lpuEuqWlBatXr+7wdFZrmJmamsLExAQZGRkdzv/Y0NDAzZuooaGBadOmwcPDo801vKamJi7QpFIppkyZ0u6pySdrb73+VVpaCktLS0ybNo2b/5Exhnv37uH06dOoqKjApEmTujQf6bM8ePAAO3fu5K5nAX+EaVlZGXfElpeXh9raWty9exeNjY146aWXMGXKFG65o9raWsTExHALWj5+/BiDBw/G6tWrMXXqVPzyyy+wtLTEsmXLBtzMHD1FQaYACjKiDloHEfj4+GDmzJnt7tM6+KP1F2ddXR127NgBgUCAl19+ucNf/K1hNnjwYFhZWeHq1avw9vbGzJkz2w3Nuro6XLp0CVevXoVQKISnpyfc3NzazPTe1NTEHcm1npr09PTkRunV1dXh6tWruHr1KhobGzF69Gh4eHjA1taWa7ekpAQxMTHIzc3FiBEjEBAQ0GaKre4Qi8X48ccfYWhoiFWrVnV4WpIxhpMnT+Lo0aPciL+amhoIBAJYWFhAU1MT+fn5uHXrFnJzc2Fubo5169YhJCQEQqGQu4Hd1dUVc+fO7XHdAwkFmQIoyIi6uHTpEmJjYxEQECC3LtmT7t+/j4iICG5Bx8ePH2PHjh0QCoVYtWpVh0dFrWFmZmYGe3t7nDt3Di4uLggODu5whora2lpcuHAB165dg7a2doczUjx9anLq1KloaGhAWloaNDU1MWnSJHh4eMidAq2trcXZs2eRmpqKwYMHw9/fH46Ojr12e8qpU6dw/fp1rFu3rtMbvO/evYt9+/Zh+vTpmDVrFhhjqKysxMWLFxEdHY27d++iqqoKWlpamDp1KiwtLVFcXAxra2ssWLAADg4OuHbtWrfnYRzIKMgUQEFG1ElcXBwuXryI+fPnY9KkSe3uc/nyZZw+fRqhoaEYN24cKioqsHPnTohEIqxatarNYI1WT4aZs7MzoqOjMXr0aLz44oudzvdXXV2NCxcucHMETp8+Ha6urm0CrfXUZHJyMnR0dODu7g4XFxe560disRiJiYm4ePEihEIh/Pz84Orq2qun5fLy8rBjx45nLqHTukBm62AbgUAgtyqzhoYG6urquGnkJBIJ8vLy8PDhQ9y7dw/Nzc1wdnbG/PnzUVhYiIyMDCxfvlxuln3SMQoyBVCQEXXCGMPx48dx/fp1LFq0qN0bXBljOHz4MDIzM7n5AsvKyrBz504YGhpi5cqVHd6b9mSYeXh44MiRIxg+fDiWLFnyzEUiHz9+jPPnzyM1NRV6enrw9vaGi4tLmxBsaWmBhoaG3JEeYww3b97EmTNn0NDQAA8PD3h7e3dYZ3dJJBL8+OOP3D16HR1tNjQ04JdffsGgQYOwZs0alJaW4uzZs8jNzeVGgpaVlWHChAmYO3euXBjX1NTgwYMHOHfuHM6cOYPa2lrY2dmhubkZGhoaeOWVVzBx4kQahv8MAybIvvjiC+4HViQSwdPTE19++WWHs4G3h4KMqBuZTIZDhw4hMzMTy5Yta/cv/CcHf7z66qsQiUR49OgRdu7cicGDByMsLKzDYHoyzHx9fXHo0CGYmJhg2bJlXRqBWFlZiXPnziEtLQ2Ghobw8fHpdBmSBw8eICYmBsXFxRg3bhxmz57dZ7OFxMTE4OrVq3jttdc6PKUolUrx22+/4dGjRwgODsaNGze4VZlHjx6NtLQ0NDU1Ye7cuZgwYUKnpzsbGxtx4sQJxMfHQyqVorCwEC0tLXBxccGQIUPklq4xNjammX2eMGCCLDAwEIsXL8bUqVPR0tKCDz/8EOnp6bh9+/Yzp31pRUFG1JFUKsXevXtRUFCAlStXtjstVVVVFbZu3Yphw4Zh6dKl0NDQQFFREXbt2oWhQ4di2bJlbe6VavVkmPn7++PAgQMQCoUICwvrcsiUl5cjISEBGRkZMDIygq+vLyZOnMgdBVVUVCA2NhaZmZkYNmwYAgICOpwrsjfk5+djx44deO655zqdc/DkyZNISEiAtbU1KioqYGZmBm9vb5SVleHSpUvc9S9FZjJ5+PAhjh8/jtzcXBQVFWH06NGYPHkyCgoK8OjRI25lgieDzdTUdEAH24AJsqeVlZVhyJAhOHfuHHx8fLr0Ggoyoq7EYjEiIiJQXl6O1atXtztjfk5ODn777TdusALwx43WERER3CnDju7DejLMgoOD8fvvv0MsFiMsLKzDRTfbU1paioSEBNy+fRumpqbw8fFBSUkJkpOTYWBggNmzZ2P8+PF9+ktbIpHgp59+gkgkwurVqzs8pRgXF4etW7fC2NgY48aNg6+vL4YNG4aoqCiUlJRgxowZ8PLy6tZ6gDKZDMnJyTh48CDS09Mxf/58rFu3Dk1NTdwiunl5eSguLgZjDAYGBrC1teXCzczMbEAF24ANsuzsbDg6OiI9PR3jx49vd5/m5mY0Nzdzz1NTU+Hr60tBRtRSY2Mjdu7ciYaGBqxZs6bdo4TW0Y4vvfQSnJycAPwx4OG3336DnZ0dFi1a1OFgjifDbMGCBThw4ACqqqqwZMkS2NraKlRrSUkJEhISkJmZCW1tbXh7e8Pd3Z2XZYpOnz6N5ORkrFu3rt3ZTqqqqnDgwAHs27cP9vb2eO211zBx4kTcvHkT0dHRMDQ0xIIFCzqckFkRNTU1+O6773D69GnMnDkTb7zxhtxpzqamJhQUFHD3shUVFXGziTwZbEOGDOnXwTYgg4wxhnnz5qGqqgoXLlzocL+NGzdi06ZNbbZTkBF1VVtbix07dgAAVq9e3eY6FmMMBw8exL179/DKK69wczDev38fe/fuhYODA0JDQzu8htUaZubm5ggNDUVUVBQKCgoQGhqq0PXoVhUVFRCJRB2OnuxtBQUF+PXXXzF79mx4eXnJfa11xOWlS5eQlpYGV1dXfPjhh2hpacGxY8eQlZUFV1dXBAQEdHgatru2bt2KqKgoODk5ISQkBF5eXu3+QSEWi+WCrbCwEFKpFCKRSC7YLCws+tXK8QMyyN544w2cOHECFy9exPDhwzvcj47ISH9UVVWFX3/9FXp6eu3OmC8Wi7F9+3ZIJBKsXbuWG2V39+5dREZGYsyYMXjxxRc7/EX4ZJgtXrwYx48fR1ZWFkJCQjq8DUAVSCQS/Pzzz9DR0ZE7pVhXV4cLFy4gJSUFmpqaqKiowPDhw/GnP/0JhYWFOHr0KGQyGUJCQvps6ROpVIpdu3YhMTERZmZmsLKywty5c585PF8ikeDhw4dcsD18+BAtLS3Q0dGBjY2N3LRa6hxsAy7I1q9fjyNHjuD8+fMdrpLbEbpGRvqL0tJS7NixA+bm5ggLC2tzyq6yshLbtm3jro21/pK7c+cODhw4gAkTJmD+/Pkdnq56MsyWLl2KuLg4XLt2Dc8991ybIx1VERsbi6SkJKxbtw7m5uZoaGjApUuXkJycDE1NTUybNg15eXkoLCzEypUrkZqaiuTkZDg4OGDevHl9vtRKY2MjfvnlF9TV1WHw4MEoKiqCs7Mz/P39uzxHZUtLCwoLC7lgKygogEQigZaWllywWVlZqdU0WQMmyBhjWL9+PaKiopCQkCA3c3dXUZCR/uThw4fYvXs3bG1t250xPzs7G3v27OGmoGp169YtHDp0iJvNoythtmzZMly+fBnnz5/nZhJRpWs2Dx8+xPbt2zFr1ixMmTIFiYmJSExMBPDfiY8vX76MixcvYvbs2UhNTUVVVRWee+45uLm58fZZysvL8csvv2DYsGEYN24c4uLiIJPJMHv2bLi6uipch1QqRVFRERds+fn5EIvFcHNzw5w5c/roU/S+ARNkr7/+Ovbu3YujR4/Knas3MjLq8mzTFGSkv7l//z727NmDsWPHYsGCBW1OL128eBFxcXFYtGgRxo4dy21PTU3FkSNH4O7ujsDAwC6F2fLly5GamopTp05h0qRJCAkJUYnTWS0tLfj5558BAOPGjUNSUhKkUqncUjTp6ek4ePAgLC0tUVZWBjMzM7z44osdruPWl3JycrBnzx64ubnBx8cHcXFxuH79OoYPH47g4OAezS8pk8lQXFwMbW3tDpf1UUUDJsg6+kHbsWMHVq1a1aX3oCAj/dGdO3fw+++/c5PVPvmzwhjDgQMHkJ2djbVr18oN22+dF/BZR1iFhYWIiIjgwuzu3buIioqCo6MjFi5cyMtIxM5ER0fj0KFDsLS0hFAo5JaUaT1VWFhYiJ9++gnV1dUwMTGBl5cXZs6c2elUXH0tOTkZJ0+eREhICFxcXJCfn4/jx4+jrKwMHh4e8PPze+bsKv3JgAmy3kBBRvqrGzdu4OjRo/D29ubuIWslFovxyy+/QCqVYu3atXKDQ65cuYJTp07B19cXM2bM6PD9nw6zgoICREZGwtLSEkuXLu316aW6oqWlBadOncL//M//wNraGvPnz4ePj4/cdFC1tbXYvHkz7t+/Dw8PDyxcuFDha+t9gTGGEydO4Pr161ixYgXs7OwglUqRlJSEhIQEiEQiBAUFYcyYMSp1CrevUJApgIKM9GetEwj7+/u3mc2isrISW7duhY2NDZYsWSL3y7H13rNZs2bB29u7w/d/OszKysqwZ88eGBoaYvny5X0+WKKVVCpFamoq4uPjER8fDxsbG3z66adtTqXV1dXhb3/7G3JycrBs2TKEhoaq1KKXT06PtXbtWm4WlcePH+PkyZO4e/cuRo0ahaCgoD6bxktVUJApgIKM9HdnzpzBhQsXuFNWT7p37x727t0LHx+fNkdf586dQ3x8fKfLxgBtw6ympgYRERHQ0NBAWFhYp8uk9JRMJkN6ejoSEhJQVVUFxhiam5vx1ltvtZl9JD8/Hxs3bkRBQQHeffddlRuc0qp1wmJNTU288sor3OlExhiysrJw8uRJNDY2wtfXF9OmTVOrkYiKoCBTAAUZ6e9aT1ldu3YNoaGh3OwerS5cuIAzZ85g8eLFcvdMMcZw9uxZXLhw4ZlraT0dZk1NTYiIiEBjYyOWL18OS0vLXv9MGRkZSEhIQHl5OcaMGYOxY8fi6NGj8PX1ha+vL7evVCrF+fPnuem83nvvPZW9XaBV6xIyrUfLTw6gEYvFSEhIQFJSEgYPHozg4GDY2tqCMQapVAqxWIzm5maIxWK5h4mJSa8sSsoXCjIFUJCRgUAmk+Hw4cO4c+dOmxnzGWP4/fffcf/+fbzyyitygz8YY4iJiUFSUhLmzZuHyZMnd9jG02EmlUqxZ88elJeXY8mSJbCzs+vx52g9KomPj8ejR4/g6OiIGTNmwMLCAj///DM0NDSwdu1a7iilsrIShw8fRlpaGmpra7Fs2TLMnj27x3X01LNCRywWIycnBydOnMDo0aMxceLENvuVlZXhxo0bqKiogIWFBWxsbDodrDJt2jQEBATw+Cl7hoJMARRkZKCQSqXYv38/8vLysGLFCrkZcJqbm/HLL79AJpO1GfzBGMPJkyeRkpKCF154Ac7Ozh228XSYCQQCREZG4sGDB1i4cKHccH9FMMaQk5ODs2fPoqioCCNGjMCMGTO42fLj4+Nx4cIFvPrqqxg6dCgYY7hx4waio6PBGENNTQ0mT56MRYsWKXw6sTV0Ogqc9h5d2Vcmk3XaroaGBh49eoTs7GxMnjwZ9vb20NLSknsIhULk5eUhNTUVgwYNwvTp0+Hs7Axtbe02+2pra6vVaUgKMgVQkJGBRCKRICIiAmVlZXj55Zfl7pmqqKjAtm3buJupnx6yf+zYMaSmpmLhwoUYN25ch208HWaampo4cuQIMjIyEBwcDFdXV4Vqzs3NxdmzZ1FQUABra2vMnDlTbpRhcXExtm3bBh8fH/j6+qKmpgZHjhzB7du34eDggPz8fAiFQsybNw8AFA6croZOe+HRXpg8a58nQ4cxhv/93//FzZs3sWLFig4naq6rq0NsbCxu3rwJGxsbBAcHK+V+uN5EQaYACjIy0DQ1NWHnzp2or6/H6tWr5Ua/3b17F/v27YOvry/8/PzkXieTyRAVFYWMjAwsWrSo0wmDnw4zoVCI6OhoJCcnY9asWZg+fTqAP4bLdxQe+fn5uHz5MgoKCrhlVczMzCCRSLh9mpqakJCQwC1WWVpaitu3b0Mmk8HR0RHFxcWor6+Hq6srd5TZV6HTV6RSKSIiIlBaWio3krE9ubm5OHHiBCorK+Hp6QkfH59en+yYLxRkCqAgIwNRXV0dfv31VzDGsHr1arlh8ufPn8fZs2exZMmSNmElk8lw8OBBZGVlYcmSJXBwcOiwjcLCQuzevRsWFhbcIp7nzp1DQkICtLW1IZFI2j3Sqa2tRW5uLiorK6Gvr4/Ro0dj2LBh7QZLVlYWMjMzERwcjIcPHyIzMxO2trYIDAxEamoq7ty5gyVLlnCn5dTt9FqrhoYGbNu2DUKhEGvWrOn0xuiWlhZcunQJFy5cgL6+PubMmYNRo0bxWG3voCBTAAUZGageP36M7du3Q1dXF6tWreLup2KMITIyErm5uVi7dm2be7GkUikiIyNx//59LFu2rNObiZ8OM21tbWRlZaGioqJNKFVXVyM5ORm5ubkYMmQIZs2aBWdn5w6vaZWUlGDr1q0YN24cHj16hMrKSsyePRvu7u5ISUnBiRMn8Pzzzyt8KlNVlZaWYvv27dwacs+aCqyyshInTpxATk4Oxo4di8DAQLkbw1UdBZkCKMjIQFZWVoZff/0VZmZmCAsL405DNTc3Y9u2bQCAtWvXtjkCaGlpwb59+5Cfn4+wsDBu0EV72guzJ5WXlyMhIQEZGRkwNjaGr68vnJ2dO/1FLZVKsXXrVty/fx/6+vowNzfHiy++CAsLC+Tm5iIiIgJTp05FUFBQd7tGJbXe99c6hdiztN6mEB0djfHjxyMwMJCHKnsHBZkCKMjIQFdYWIhdu3bB2toaS5Ys4YZwl5eXY9u2bRgxYkS7o/0kEgn27NmD4uJirFixotPVk9sLs6qqKpw7dw43b96EgYEBfH19MWnSpC6d+jt16hRXm7+/P2bNmoVBgwZxS9VYWlpi+fLlKjGBcW9LTExETEwM5s+f3+W14JqamiAQCNRqrkYKMgVQkBHyxyCB3377rc0Cm1lZWdi3bx9mzJghd5NxK7FYzI2CXLVqVac33D4ZZubm5rhx4wZ0dXXh7e0NV1fXLk/Ye+HCBXz66acYOXIk3n33Xdjb2wP47y0ErfNHqtLUU72pdQRpWloaVq1aBWtra2WX1Cf6358ghJA+NWLECISGhuL27ds4ceIEWv8WHj16NPz8/JCQkIC7d++2eZ2WlhaWLVsGU1NT7N69G6WlpR22MWzYMKxYsQKPHj3CnTt3MHv2bPzlL3+Bu7t7l0JMLBYjKioKX375JaytrbFlyxYuxGQyGQ4dOoSamhosWbKk34YY8MeqH3PnzsWwYcOwf/9+PH78WNkl9Qk6IlMAHZER8l+ta5JNnz6dmwGDMYb9+/fjwYMHePXVV9udO7GxsRG7du1CXV0dXn755U7nV6yrq+MGeHTVw4cPcfjwYdy6dQsaGhr46KOP5G7ojouLw6VLl7B06dJuLbCrjurr67Ft2zZoa2tjzZo1ajvMviN0REYI6ZZJkyYhMDAQFy9exKVLlwD8cQTwwgsvwMDAAPv370dzc3Ob14lEIoSFhUEkEmHXrl2oqqrqsA19ff0u/9KVyWRISEjAr7/+ipaWFgwePBgLFy6UC7G0tDRcvHgRzz333IAJMQDQ09PD0qVLUVVVhcOHD6O/Hb9QkBFCus3DwwO+vr6IjY3FtWvXAAA6OjpYvHgxN2tGe7809fT0sGLFCgiFQuzatQvV1dU9qqOqqgo7duzAuXPnuFWdhw8fLnej9sOHD3Hs2DFMnDix0xn6+6shQ4Zg4cKFyMrKwtmzZ5VdTq+iICOE9Iifnx/c3Nxw/PhxZGRkAADMzMzwwgsv4M6dO7h48WK7rzMwMMDKlSshEAiwa9cu1NbWKtw2Ywypqan48ccfUVdXh9WrV0NbWxuPHj3CvHnzuOtpNTU1iIyMxNChQ/H888+r5JIsfBg1ahRmz56NCxcuIC0tTdnl9BoKMkJIjwgEAgQFBWH8+PE4fPgwcnJyAABjxoyBr68vzp49i3v37rX7WkNDQ6xcuRJSqRS7du1CfX19l9ttbGzEgQMHcOTIETg5OWHdunXQ0dFBfHw8PD09uVOKEokE+/fvh0AgwOLFi7s84rG/8vT0xKRJk3Ds2DE8fPhQ2eX0CgoyQkiPCQQCzJ8/H/b29ti/fz8KCgoA/HG05ujoiEOHDqGysrLd1xobG2PFihVobm7G7t270dDQ8Mz27t+/jx9//BG5ubl46aWXMH/+fAiFQhw5cgQmJibcwp+MMRw9ehRlZWVYsmQJ9PX1e+9DqymBQIDg4GBYWlpi//79PT6tqwooyAghvUJTUxOhoaGwsrLCnj178OjRIwgEAixYsAB6enrYv38/xGJxu68dPHgwVqxYgbq6OkRERKCpqand/VpaWnD69Gns3r0bZmZmeO2117jFPxMTE1FUVIT58+dzR10XL17ErVu3MH/+/F5fsFOdDRo0CIsXL4ampib27dvX4fdFXVCQEUJ6jVAoxJIlS2BsbIyIiAhUVlZygz8eP37c4eAPADA3N8eKFSvw+PFj/Pbbb21GPJaWlmLbtm24cuUK/P39ERYWBkNDQwB/zCwSHx+PadOmcacUMzMzcebMGfj6+na6lMxA1TqSsbKyElFRUWo9kpGCjBDSq3R0dBAWFgZtbW1ERESgtrYW5ubmeOGFF3D79m1uqH57LCwsEBYWhrKyMuzduxdisRiMMVy5cgVbt27lFvP09PTkBmzIZDIcOXIERkZG3CnFR48e4fDhwxg7dmybJWbIf1lYWGDBggXIzMxEfHy8ssvpNgoyQkiv09PTQ1hYGLc+VmNjI8aOHQsfHx+cOXMG2dnZHb7WysoKy5cvR3FxMfbt24c9e/bg1KlTcHV15VZ1flJSUhIKCwsxb948CIVCNDQ0YN++fTAxMcELL7wwYEcodtWYMWMwa9YsnD9/Hunp6coup1soyAghfaJ1EEddXR327NkDsVgMPz8/ODg44ODBgx0O/gAAa2trLF26FA8fPkRJSQmWL1+OoKAgCIVCuf0qKipw9uxZeHh4wMbGBlKpFL///jskEgmWLFnS72aw6CteXl5wdnbG0aNHUVhYqOxyFEZBRgjpM2ZmZli+fDnKysoQGRkJmUyGF198Ebq6up0O/gAAOzs7vPbaa3jjjTfaXZRTJpPh6NGjMDQ0xMyZM8EYw6lTp1BQUIBFixbB2Ni4Dz9Z/yIQCBASEoKhQ4di3759qKmpUXZJCqEgI4T0KSsrKyxZsgR5eXk4fPgwtLS0uMEfR48e7XSQgampaYeT+iYnJ6OgoIA7pXj16lWkpKRg7ty5na55Rtr35EjGjm5iV1UUZISQPmdnZ4fQ0FBkZmbi+PHjMDc3x/z585GRkYHLly8r/H4VFRU4c+YM3NzcYGtri/v37yM6OhoeHh40oXcP6OvrY9WqVQgICFB2KQqhICOE8GL06NGYN28erl+/jtjYWIwdOxbe3t6Ii4vjZgPpitabnPX19TFr1ixUVlbiwIED3MKZpGdMTEy6tGCpKqEgI4TwZuLEiQgMDMTly5dx6dIlzJgxA/b29jh48GCns+A/KTk5Gfn5+Zg3bx5kMhn27dsHXV1dLFy4sF+u8kyejb7rhBBeeXh4wM/PD3Fxcbh+/TpefPFF6OjoPHPwBwBUVlYiLi4Obm5usLGxwaFDh1BbW9vvF8gknaMgI4TwztfXF+7u7jhx4gRycnKwePFiVFZW4tixYx0O/mCM4dixY9DX18fs2bO5+9EWLlwIMzMznj8BUSUUZIQQ3gkEAgQGBmLChAmIiopCbW0t5s+fj1u3biExMbHd11y9ehUPHjxASEgI7ty5g0uXLsHf37/doflkYKEgI4QohUAgwLx58+Dg4IDIyEgYGBhg+vTpiI2Nxf379+X2raqqQlxcHKZOnQqhUIhjx45h8uTJ8PDwUFL1RJVQkBFClEZTUxMLFy7EsGHDsHfvXjg5OWHkyJE4cOAAN/ijdZSirq4u3NzcsH//flhZWWHu3Lk0/RQBQEFGCFGy1hnzTUxMsHfvXsycORM6OjqIjIyERCJBSkoKHjx4gKCgIBw+fBiamppYtGjRgF8gk/wXBRkhROm0tbWxfPly6Ojo4MCBA5g7dy4qKipw4MABxMbGwtXVFenp6SgvL8fixYtpgUwih4KMEKISWmfMZ4whJiYG/v7+uHv3LkQiEUQiEW7duoUXXniBFsgkbVCQEUJUhpGREcLCwlBfX4/U1FQEBwfD1dUVFy9ehJ+fH7caNCFPoiAjhKgUMzMzhIWFoby8HDdv3sTFixfh5OQEX19fZZdGVNSACrLz58/j+eefh5WVFQQCAY4cOaLskggh7bC0tMSSJUtQXFwMU1NTzJ8/n0Yokg4NqGE/9fX1mDhxIl5++WW8+OKLyi6HENKJ1vXI9PT0aIFM0qkBFWRBQUEICgpSdhmEkC4aPHiwsksgamBABZmimpub0dzczD2vq6tTYjWEEELaM6CukSnqiy++gJGREfegi82EEKJ6KMg68cEHH6C6upp7nDt3TtklEUIIeQqdWuyEtrY2tLW1uec0mwAhhKgeOiIjhBCi1gbUEVldXR2ys7O557m5uUhNTYWpqSlsbGyUWBkhhJDuGlBBlpKSghkzZnDP3377bQDAypUrsXPnTiVV1X3FxcUoLi5WdhmEkH7G0tJSrea0FLCO1hUnbRQXF+Pnn3/Gn/70J6V/k5ubmxEQEEADUAghvc7X1xcxMTFyYwRUGQWZmqqpqYGRkRHOnTtHg1B4UldXB19fX+pzHlGf86+1z6urq2FoaKjscrpkQJ1a7I8mTZqkNv/Z1F1NTQ0A6nM+UZ/zr7XP1QmNWiSEEKLWKMgIIYSoNQoyNaWtrY3w8HC1uRjbH1Cf84/6nH/q2Oc02IMQQohaoyMyQgghao2CjBBCiFqjICOEEKLWKMgGqISEBAgEAjx+/FjZpRBCSI9QkPWCkpISrF+/HiNHjoS2tjasra3x/PPP48yZM73ajp+fH958881efc/ObN26FX5+fjA0NFTb0BMIBJ0+Vq1a1e33trOzw7fffvvM/fpDPypC2X1eWVmJ9evXY/To0dDV1YWNjQ02bNiA6urqbrer6pTd5wDwpz/9Cfb29hCJRDA3N8e8efOQmZnZ7XYVQTN79NCDBw/g5eUFY2NjbNmyBc7OzpBIJIiJicEbb7zB2zeyFWMMUqkUgwb1/Fvb0NCAwMBABAYG4oMPPuiF6vj35KTKkZGR+OSTT5CVlcVtE4lEfV5Df+hHRSi7z4uKilBUVISvv/4aTk5OyMvLw7p161BUVISDBw/2advKouw+BwBXV1csW7YMNjY2qKysxMaNG+Hv74/c3Fxoamr2beOM9EhQUBAbNmwYq6ura/O1qqoq7t95eXksJCSE6enpMQMDAxYaGspKSkq4r4eHh7OJEyey3bt3M1tbW2ZoaMgWLVrEampqGGOMrVy5kgGQe+Tm5rL4+HgGgEVHRzNXV1cmFArZ2bNnWVNTE1u/fj0zNzdn2trazMvLiyUnJ3Pttb7uyRo7osi+qmzHjh3MyMhIbtuxY8eYi4sL09bWZiNGjGAbN25kEomE+3p4eDiztrZmWlpazNLSkq1fv54xxpivr2+b78ez9Jd+VISy+7zV77//zrS0tOTa6a9Upc9v3rzJALDs7Oxe+VydoSDrgYqKCiYQCNjnn3/e6X4ymYxNnjyZTZ8+naWkpLCkpCTm4uLCfH19uX3Cw8OZvr4+W7BgAUtPT2fnz59nQ4cOZX//+98ZY4w9fvyYTZs2ja1du5YVFxez4uJi1tLSwv1ydHZ2ZqdPn2bZ2dmsvLycbdiwgVlZWbGTJ0+yjIwMtnLlSmZiYsIqKioYYxRkjDEWHR3NDA0N2c6dO1lOTg47ffo0s7OzYxs3bmSMMXbgwAFmaGjITp48yfLy8tiVK1fY1q1bGWN/fO+HDx/ONm/ezH0/nqW/9KMilN3nrbZt28bMzMx69bOpKlXo87q6Ovbmm2+yESNGsObm5l7/jE+jIOuBK1euMADs8OHDne53+vRppqmpyfLz87ltGRkZDAB3lBQeHs50dXW5IzDGGHv33XeZu7s799zX15f95S9/kXvv1l+OR44c4bbV1dUxoVDI9uzZw20Ti8XMysqKbdmyRe51AznIvL292/wREhERwSwtLRljjP3rX/9io0aNYmKxuN33s7W1Zd98802X2+8v/agIZfc5Y4yVl5czGxsb9uGHHyr0OnWlzD7//vvvmZ6eHgPAxowZw8vRGGOM0WCPHmD/NymKQCDodL87d+7A2toa1tbW3DYnJycYGxvjzp073DY7OzsYGBhwzy0tLVFaWtqlWqZMmcL9OycnBxKJBF5eXtw2oVAINzc3ufYGumvXrmHz5s3Q19fnHmvXrkVxcTEaGhoQGhqKxsZGjBw5EmvXrkVUVBRaWlqUXbZa47vPa2pqMHfuXDg5OSE8PLwXP4n64LPPly1bhhs3buDcuXNwdHTESy+9hKampl7+RG1RkPWAo6MjBALBM8OBMdZu2D29XSgUyn1dIBBAJpN1qRY9PT259219fVfqGKhkMhk2bdqE1NRU7pGeno579+5BR0cH1tbWyMrKwvfffw+RSITXX38dPj4+kEgkyi5dbfHZ57W1tQgMDIS+vj6ioqLa/HwNFHz2uZGRERwdHeHj44ODBw8iMzMTUVFRffCp5FGQ9YCpqSkCAgLw/fffo76+vs3XW4dZOzk5IT8/HwUFBdzXbt++jerqaowdO7bL7WlpaUEqlT5zPwcHB2hpaeHixYvcNolEgpSUFIXa6+9cXFyQlZUFBweHNg8NjT9+NEQiEUJCQvCf//wHCQkJSExMRHp6OoCufz/If/HV5zU1NfD394eWlhaOHTsGHR2dPv1cqkyZ/88ZY2hubu61z9IRGn7fQz/88AM8PT3h5uaGzZs3w9nZGS0tLYiNjcWPP/6IO3fuYPbs2XB2dsayZcvw7bffoqWlBa+//jp8fX3lTgk+i52dHa5cuYIHDx5AX18fpqam7e6np6eH1157De+++y5MTU1hY2ODLVu2oKGhAWvWrOlyeyUlJSgpKUF2djYAID09HQYGBrCxsemwbXXyySefIDg4GNbW1ggNDYWGhgbS0tKQnp6Ozz77DDt37oRUKoW7uzt0dXUREREBkUgEW1tbAH98P86fP4/FixdDW1sbZmZm7bbT3/tREXz0eW1tLfz9/dHQ0IDffvsNNTU13GKR5ubmfT8UXMXw0ef3799HZGQk/P39YW5ujsLCQnz55ZcQiUSYM2dO339IXq7E9XNFRUXsjTfeYLa2tkxLS4sNGzaMhYSEsPj4eG6frg6/f9I333zDbG1tuedZWVnMw8ODiUSiNsPvnx5A0NjYyNavX8/MzMy6Pfw+PDy8zdBbAGzHjh3d6CXla29YcnR0NPP09GQikYgZGhoyNzc3bsRWVFQUc3d3Z4aGhkxPT495eHiwuLg47rWJiYnM2dmZaWtrdzosub/1oyKU0eet/7fbe+Tm5vbVR1UZyujzwsJCFhQUxIYMGcKEQiEbPnw4W7p0KcvMzOyzz/kkWsaFEEKIWqNrZIQQQtQaBRkhhBC1RkFGCCFErVGQEUIIUWsUZIQQQtQaBVkfW7VqFQQCAf75z3/KbT9y5EifzrIhkUjw3nvvYcKECdDT04OVlRVWrFiBoqIiuf2am5uxfv16mJmZQU9PDyEhIXj48GGf1cUH6nP+UZ/zj/r8vyjIeKCjo4Mvv/wSVVVVvLXZ0NCA69ev4+OPP8b169dx+PBh3L17FyEhIXL7vfnmm4iKisL+/ftx8eJF1NXVITg4WO1nrKA+5x/1Of+oz/8PL3erDWArV65kwcHBbMyYMezdd9/ltkdFRSm0tk9vSE5OZgBYXl4eY+yPpWGEQiHbv38/t09hYSHT0NBg0dHRvNbWm6jP+Ud9zj/q8/+iIzIeaGpq4vPPP8f//M//KHRoHRQUJDdjdXsPRVRXV0MgEMDY2BjAH7NiSyQS+Pv7c/tYWVlh/PjxuHz5skLvrWqoz/lHfc4/6vM/0FyLPHnhhRcwadIkhIeHY/v27V16zS+//ILGxsZeab+pqQnvv/8+li5dCkNDQwB/zAGopaUFExMTuX0tLCxQUlLSK+0qE/U5/6jP+Ud9TkHGqy+//BIzZ87EO++806X9hw0b1ivtSiQSLF68GDKZDD/88MMz92f9aLkX6nP+UZ/zb6D3OZ1a5JGPjw8CAgLw97//vUv798bhv0QiwUsvvYTc3FzExsZyfzEBwNChQyEWi9tcKC4tLYWFhYViH05FUZ/zj/qcfwO9z+mIjGf//Oc/MWnSJIwaNeqZ+/b08L/1P9q9e/cQHx+PwYMHy33d1dUVQqEQsbGxeOmllwAAxcXFuHXrFrZs2dLtdlUN9Tn/qM/5N6D7vNeHjxA5K1euZPPmzZPbFhYWxnR0dPp0ZJFEImEhISFs+PDhLDU1lRUXF3OP5uZmbr9169ax4cOHs7i4OHb9+nU2c+ZMNnHiRNbS0tJntfU16nP+UZ/zj/r8vyjI+lh7/9kePHjwzDWseio3N7fDNZmeXCetsbGR/fnPf2ampqZMJBKx4OBglp+f32d18YH6nH/U5/yjPv8vWo+MEEKIWqPBHoQQQtQaBRkhhBC1RkFGCCFErVGQEUIIUWsUZIQQQtQaBRkhhBC1RkFGCCFErVGQEUIIUWsUZIQQQtQaBRkhhBC1RkFGCCFErVGQEUIIUWsUZIQQQtQaBRkhhBC1RkFGCCFErVGQEUIIUWsUZIQQQtQaBRkhhBC1RkFGCCFErVGQEUIIUWsUZIQQQtQaBRkhhBC1RkGmgOLiYmzcuBHFxcXKLoUQQsj/oSBTQHFxMTZt2kRBRgghKoSCjBBCiFqjICOEEKLWKMgIIYSoNQoyQgghao2CjBBCiFqjICOEEKLWKMgIIYSoNQoyQgghcmQymbJLUAgFGSGEEDktLS3KLkEhFGSEEELkMMaUXYJCKMgIIYSoNQoyQgghao2CjBBCiFqjICOEEKLWKMgIIYTIoeH3hBBC1BoFGSGEELUmkUiUXYJCKMgIIYTIaW5uVnYJCqEgI4QQIqexsVHZJSiEgowQQoic+vp6ZZegEAoyQgghcmpqapRdgkIoyAghhMhpbGyEWCxWdhldRkFGCCGkjerqamWX0GUUZIQQQtqoqqpSdgldRkFGCCGkDQoyQgghaq2iokLZJXQZBRkhhJA2Kioq1GaBTQoyQgghbTQ2NqK2tlbZZXQJBRkhhJB2FRUVKbuELqEgI4QQ0q6CggJll9AlFGSEEELaVVBQoBYz4VOQEUIIaVdLSwtycnKUXcYzUZARQgjpUEZGhsqPXhyk7AIIIYSojilTpiAvLw+6urr48MMPUVFRgYKCAtjY2Ci7tA7RERkhhBBOSUkJysvL5WbAv3r1qkoflVGQEUII6VRFRQVu3bql7DI6REFGCCHkmZKTk1FZWansMtpFQUYIIeSZpFIpYmNj0dzcrOxS2qAgI4QQ0iXV1dWIi4uDVCpVdilyKMgIIYR0WWFhIc6fP69Sgz8oyAghhCjk3r17SE5OVnYZnAEVZOfPn8fzzz8PKysrCAQCHDlyRNklEUKIWrp58yYyMjKUXQaAARZk9fX1mDhxIr777jtll0IIIWrv8uXLyM/PV3YZypvZQywWIzc3F/b29hg0iJ8ygoKCEBQUxEtbhBDS3zHGcObMGcyfPx8mJiZKq4P3I7KGhgasWbMGurq6GDduHJfmGzZswD//+U++yyGEENIDEokEMTExaGpqUloNvAfZBx98gJs3byIhIQE6Ojrc9tmzZyMyMpLvcjrV3NyMmpoa7lFXV6fskgghROXU1NQgNjZWacPyeQ+yI0eO4LvvvsP06dMhEAi47U5OTiq3XMAXX3wBIyMj7uHr66vskgghRCUVFxcjPj5eKcPyeQ+ysrIyDBkypM32+vp6uWBTBR988AGqq6u5x7lz55RdEiGEqKz79+/jwoULvIcZ70E2depUnDhxgnveGl7btm3DtGnT+C6nU9ra2jA0NOQe+vr6yi6JKJlMJlN2CYSotMzMTCQlJfEaZryPWvziiy8QGBiI27dvo6WlBf/v//0/ZGRkIDExsc+PeOrq6pCdnc09z83NRWpqKkxNTVV6rR2iOmQyGTQ0BtRdK4QoLD09HSKRCJMmTeKlPd5/Ij09PXHp0iU0NDTA3t4ep0+fhoWFBRITE+Hq6tqnbaekpGDy5MmYPHkyAODtt9/G5MmT8cknn/Rpu6T/oCMyQromOTmZt3EPSrmPbMKECdi1axfv7fr5+anU/GBE/ajaZKmEqLKEhAQYGxtj8ODBfdoO70dkJ0+eRExMTJvtMTExOHXqFN/lEKIQsVis7BIIURtSqRRxcXGQSCR92g7vQfb++++3+1ctYwzvv/8+3+UQohBVXIuJEFVWXV2Ny5cv92kbvAfZvXv34OTk1Gb7mDFj5AZiEKKK6uvrlV0CIWonKysLhYWFffb+vAeZkZER7t+/32Z7dnY29PT0+C6HEIVUVFQouwRC+kx+fj4aGhoA/HEavbKystfe+/Lly302WIr3IAsJCcGbb74pN5olOzsb77zzDkJCQvguhxCFVFdX01EZ6XeSk5Px/PPPw87ODlVVVQD+mBf373//O77//ns8ePCgx21UVVX12ShG3oPsq6++gp6eHsaMGYMRI0ZgxIgRGDt2LAYPHoyvv/6a73IIUVhJSYmySyCk1xw+fBheXl44depUm1HdjDHcunULX375Ja5fv97jtm7fvt3j92gP78PvjYyMcPnyZcTGxuLmzZsQiURwdnaGj48P36UQ0i0lJSWwt7dXdhmE9FhycjIWLVoEqVTa4a1JracDt23bhvfeew92dnbdbu/Ro0doaGiArq5ut9+jPUq5j0wgEMDf3x/+/v7KaJ6QHsnNzYW7uztv6+gR0lc+++wzMMa6fH/tyZMn8frrr/eozUePHmHEiBE9eo+nKeUn8cyZMzhz5gxKS0vbXPz79ddflVESIc80ZcoU5OXlQVdXFzY2NvD29la5ia4J6ar8/HwcP368yyEmk8mQlpaGyspKmJqadrvd6urqbr+2I7xfI9u0aRP8/f1x5swZlJeXo6qqSu5BiKoqKSlBeXk5ampqkJmZiYsXL9KUVURtnTlzRuGZjhhjyMzM7FG7jY2NPXp9e3g/Ivvpp5+wc+dOhIWF8d00Ib3qzp07qK6uxqxZsyASiZRdDiEKqa2thYaGhkJ/jAkEgh6vBN3S0tKj17eH9yMysVgMT09PvpslpE8UFRXh4MGDyMvLU3YphCjEwMBA4TMKjDHo6Oj0qN2+OB3Pe5C98sor2Lt3L9/NEtJnGhsbERMTgzNnznA3kxKi6mbNmqVwqAgEAowZM6ZH7fY0CNvD+6nFpqYmbN26FXFxcXB2doZQKJT7+r///W++SyKkV+Tk5KCgoABubm4YO3YsDQQhKs3GxgbBwcE4efJkl1Z10NDQwIQJE3o00ANAn8yEz3uQpaWlcYut3bp1S+5r9INP1J1YLMbFixeRnZ0Nb29vmJiYKLskQjr08ccf49SpUxAIBF0a+DFnzpwetaepqYlhw4b16D3aw3uQxcfH890kIbwrKSnBoUOHMH78eLi4uEBLS0vZJRHSxtSpUxEZGYlFixaBMdbukVnriuivvvpqj26GBgB7e/s++VlQ2prt2dnZiImJ4YZi0oKXRJV1ZzLV1vtu9u/fj4yMDBqqT1TSggULcPnyZcyZM6fNWTGBQIAJEybgvffew+TJk3vUjkAg6PF7dPjejOcEqaiowEsvvYT4+HgIBALcu3cPI0eOxJo1a2BsbIx//etffJajkOvXr8PV1RXXrl2Di4uLssshPEhOTsann36KEydOyP2x1foDPnfu3C7/lWpqagofHx8MGTKkj6olpGfy8/MxadIkVFVVQVdXFx9//HGPr4m1GjduHLy8vHrlvZ7G+xHZW2+9BaFQiPz8fLn5thYtWoTo6Gi+yyGkQ709mWplZSWOHj2KK1eudOniOiF8s7Gx4X4va2lp9VqIiUQiTJkypVfeqz28B9np06fx5ZdfYvjw4XLbHR0d6V4cojKenEy1o9CRyWSQyWTYtm1bl5e5YIzh5s2bOHToEIqLi3uxYkJU1/Tp06Gtrd1n7897kNXX17c783F5eXmfflBCFNGdyVQV8fjxY/zv//4vzp071+OZEghRZePGjev1SYKfxnuQ+fj4YPfu3dxzgUAAmUyGr776CjNmzOC7HELaaJ1Mtaun/56cTFVRWVlZOHDgQJ8uA0+IslhbW2PatGl93g7vw++/+uor+Pn5ISUlBWKxGH/729+QkZGByspKXLp0ie9yCGmjJ5Opdmf6tcbGRkRHR2PevHkwMzNT+PWEqCJLS0s899xz3PD9vsT7EZmTkxPS0tLg5uaG5557DvX19ViwYAFu3LhBixUSldA6maoiejqZqlQqxb1797r9ekJUydChQxEYGMjbmn28HpFJJBL4+/vj559/xqZNm/hsmpAuU9ZkqnSfGekPhg4diqCgoDbTD/YlXo/IhEIhbt26RVNREZWmrMlUbW1te/R6QpTN3NwcgYGBvIYYoIRTiytWrMD27dv5bpaQLmudTFVTU7NL+2toaMDZ2bnb99wIBAJ4eXm1uSWFEHViZGSEwMBApUzHxvtgD7FYjF9++QWxsbGYMmUK9PT05L5Os98TVcDXZKrGxsbw8/Oj2T6IWtPW1kZgYKDSFpjlPchu3brFTe909+5dua/RKUeiKvp6MlVNTU1MmjQJkyZN6vKRHyGqSCAQYNasWTAyMlJaDV0OMhMTky4HTWf309Ds90RdtE6m+umnn+L48ePtzrU4Z84chUJMIBDA3t4eU6dOhYGBQR9UTQi/3NzclH5avMtB9u2333L/rqiowGeffYaAgADuZrfExETExMTg448/7tL7ZWdnIycnBz4+PhCJRGCM0REZUTlTp07FsWPHejyZqqamJhwdHeHs7AxjY+O+K5gQHtnb28PZ2VnZZXQ9yFauXMn9+8UXX8TmzZvx5z//mdu2YcMGfPfdd4iLi8Nbb73V4ft0NPv9K6+8ovKz35OBq3Uy1aqqKoUmU9XS0sK4ceMwfvx4pV0/IKQvmJmZwdfXVyUOQLo1ajEmJgaBgYFttgcEBCAuLq7T19Ls92QgaD31uGTJEkydOpVCjPQrIpEI/v7+vN3w/CzdCrLBgwcjKiqqzfYjR45g8ODBnb6WZr8n/Z2pqSnmz5+PadOm0UTYpN8RCASYPXs29PX1lV0Kp1txumnTJqxZswYJCQncNbKkpCRER0fjl19+6fS1NPs96a+EQiFcXFwwfvx4GolI+q0pU6bA0tJS2WXI6dYR2apVq3D58mUYGxvj8OHDOHToEIyMjHDp0iWsWrWq09fS7PekPxo9ejQWL16MiRMnUoiRfsvCwgKTJk1SdhltdPsEp7u7O/bs2aPw62j2e9Kf6OrqYubMmbCyslJ2KYT0KYFAgOnTp6vE4I6ndXuKqpycHHz00UdYunQpSktLAQDR0dHIyMjo9HU0+z3pL0QiEUJCQijEyIBgb2//zDEQytKtIDt37hwmTJiAK1eu4NChQ6irqwMApKWlITw8vM3+CxYsQE1NDQBg9+7dMDExwaZNm3D8+HGcPHkSn332mcqdcyXkaUOHDoWZmRkMDQ0BAH5+fty/CenvVPGUYqtuBdn777+Pzz77DLGxsXITRM6YMQOJiYlt9j9+/Djq6+sBAC+//DKqq6u7WS4hypOSkoL9+/fjww8/hL29PaytrZVdEiG8sLKy6vak2Hzo1jWy9PR07N27t812c3NzVFRUtNk+ZswYfPDBB5gxYwYYY/j99987/Et2xYoV3SmJEN4IhUJ4eHgouwxCeOPk5KTsEjrVrSAzNjZGcXExRowYIbf9xo0bGDZsWJv9f/zxR7zzzjs4ceIEBAIBPvroo3YvGAoEAgoyovKcnJzarNpASH8lEolUfq28bgXZ0qVL8d577+HAgQPc8PlLly7hr3/9a7tB5OXlhaSkJAB/zBh+9+5dWraCqC0HBwdll0AIbxwcHFT+lpJuXSP7xz/+ARsbGwwbNgx1dXVwcnKCj48PPD098dFHH7XZ/8nBHjt27KBZv4naEgqFKn2tgJDepg5/uCl8RMYYQ1FREbZt24ZPP/0U169fh0wmw+TJk+Ho6Njua1oHexgaGmL16tUICgqiueeIWjIyMlLJ+2gI6S1Dhw5Fc3MzdHV1oa+vDzMzM2WX9EzdCjJHR0dkZGTA0dERI0eOfOZraLAH6S9oCRbS36WkpODs2bPIzs7GsGHD1OIPN4WDTENDA46OjqioqOjwCOxpP/30E95++22VGOzxww8/4KuvvkJxcTHGjRuHb7/9Ft7e3n3aJuk/6LQ4GUjU5Wb/bl0j27JlC959913cunWrS/t7enoiKSkJZWVlYIzh7t27qKqqavPobGXp3hAZGYk333wTH374IW7cuAFvb28EBQUhPz+/T9sl/YeOjo6ySyCEN0OHDlV2CV3SrSBbvnw5kpOTMXHiRIhEIpiamso9OpObmwtzc/NuFdtT//73v7FmzRq88sorGDt2LL799ltYW1vjxx9/VEo9RP2o+ugtQnqLSCRSqaVaOtOt4ffffvutQvunpaVh/Pjx0NDQQHV1NdLT0zvct6+WzRaLxbh27Rref/99ue3+/v64fPlyu69pbm5Gc3Mz97x1Kq6WlhZIJJI+qZOoNqlUSt970u+1tLTA1NQULS0tyi4FQqHw2TsxHggEAvbo0SPu3xoaGkwgEHCP1ucaGhp9VkNhYSEDwC5duiS3/R//+AcbNWpUu68JDw9nAOhBD3rQgx5KenRFl4/IampquJGGrfeEdeTpEYlPnk7Mzc3tapN94ulBJoyxDkflfPDBB3j77be556mpqfD19cWVK1cwefLkPq2TqKbO/r8Q0l/Ex8fDwcFBbeYT7XKQmZiYoLi4GEOGDIGxsXG7P8ytP+RSqVRu+5PTmyhrqhMzMzNoamqipKREbntpaSksLCzafY22trbcqtWt54sHDRrUtcNdQghRQ4MGDYKFhYXa/J7rcpCdPXuWG8gRHx+vUCPHjh3r8r4hISEKvXdXaWlpwdXVFbGxsXjhhRe47bGxsZg3b16ftEkIIepo0KBB0NXVVXYZXdblIPP19W33310xf/58uecCgQCMMbnnrZ4+mutNb7/9NsLCwjBlyhRMmzYNW7duRX5+PtatW9dnbRJCiLrR19dXq1Po3Rq12KqhoQH5+fkQi8Vy258eeSiTybh/x8XF4b333sPnn3+OadOmQSAQ4PLly/joo4/w+eef96ScZ1q0aBEqKiqwefNmFBcXY/z48Th58qTKz+xMCCF8UrcpBAXsyUOjLiorK8PLL7+MU6dOtfv1zo6qxo8fj59++gnTp0+X237hwgW8+uqruHPnjqLl8Ob69etwdXXFtWvX4OLiouxyCCGkT+Tk5MDe3l7ZZXRZt26IfvPNN1FVVYWkpCSIRCJER0dj165dcHR0fOb1sJycHBgZGbXZbmRkhAcPHnSnHEIIIb1IS0tL2SUopFtBdvbsWXzzzTeYOnUqNDQ0YGtri+XLl2PLli344osvOn3t1KlT8eabb6K4uJjbVlJSgnfeeQdubm7dKYcQQkgvUpfRiq26FWT19fXcwpimpqYoKysDAEyYMAHXr1/v9LW//vorSktLYWtrCwcHBzg4OMDGxgbFxcXYvn17d8ohhBDSi9QtyLo12GP06NHIysqCnZ0dJk2ahJ9//hl2dnb46aefYGlp2elrHRwckJaWhtjYWGRmZoIxBicnJ8yePVutRskQQkh/NSCC7MlTg+Hh4QgICMCePXugpaWFnTt3PvP1AoEA/v7+8Pf3707zhBBC+tCgQT0a0M47haptaGjAu+++iyNHjkAikeD06dP4z3/+gwcPHiAzMxM2NjZqsZooIYSQjqnbKg8KXSMLDw/Hzp07MXfuXCxZsgSxsbF47bXXoKurCxcXFwoxQgjpBzQ0ujV8QmkUOiI7fPgwtm/fjsWLFwMAli1bBi8vL0ilUrVLcEIIIe1Tt/EKCsVuQUEBvL29uedubm4YNGgQioqKer0wQgghytGNeTKUSqEjMqlU2uZGuUGDBim8+JpMJkN2djZKS0vlpq8CAB8fH4XeixBCSO/q10HGGMOqVavkljZpamrCunXroKenx207fPhwh++RlJSEpUuXIi8vr01ntbcEDCGEEH716yBbuXJlm23Lly9XqMF169ZhypQpOHHiBCwtLdXuXCwhhPR3T58pU3UKBdmOHTt63OC9e/dw8OBBODg49Pi9CCGEEN7HWLq7uyM7O5vvZgkhhPRTvN++vX79erzzzjsoKSnBhAkT2kyF8vRaZoQQQvilbpd8eA+yF198EQCwevVqblvritE02IMQQpRP3e4L5j3IcnNz+W6SEEKIAgbEpME9YWtry3eThBA1RjMHkWdR2hTHt2/fRn5+PsRisdz2kJAQJVVECFFF6na9hvCP9yC7f/8+XnjhBaSnp3PXxoD//mela2SEkCdRkJFn4X34/V/+8heMGDECjx49gq6uLjIyMnD+/HlMmTIFCQkJfJdDCCFEzfF+RJaYmIizZ8/C3NwcGhoa0NDQwPTp0/HFF19gw4YNuHHjBt8lEUJUmEwmo2tkpFO8H5FJpVLo6+sDAMzMzLiZ821tbZGVlcV3OYQQFSeRSJRdAlFxvB+RjR8/HmlpaRg5ciTc3d2xZcsWaGlpYevWrRg5ciTf5RBCVFxjYyN0dHSUXQZRYbwH2UcffYT6+noAwGeffYbg4GB4e3tj8ODBiIyM5LscQoiKq6urg4mJibLLICqM9yALCAjg/j1y5Ejcvn0blZWVMDExodFJhJA2ampqlF0CUXG8XyNrlZ2djZiYGDQ2NsLU1FRZZRBCVBwFGXkW3oOsoqICs2bNwqhRozBnzhwUFxcDAF555RW88847fJdDCFFxjY2NbSZOIORJvAfZW2+9BaFQiPz8fOjq6nLbFy1ahOjoaL7LIYSogcrKSmWXQFQY79fITp8+jZiYGAwfPlxuu6OjI/Ly8vguhxCiBvLy8jB06FBll0FUFO9HZPX19XJHYq3Ky8uhra3NdzmEEDVw584dNDY2KrsMoqJ4DzIfHx/s3r2bey4QCCCTyfDVV19hxowZfJdDCFEDYrEY8fHx3NyshDyJ91OLX331Ffz8/JCSkgKxWIy//e1vyMjIQGVlJS5dusR3OYQQNfHw4UNcvnwZnp6edKsOkcN7kDk5OSEtLQ0//vgjNDU1UV9fjwULFuCNN96ApaUl3+UQQlTYlClTkJeXB11dXXz44YfIyMiArq4uJk+erOzSiApRynpkQ4cOxaZNm5TRNCFEjZSUlKC8vBzGxsbctqtXr0JXVxejR49WXmFEpSglyJqampCWlobS0lLIZDK5r9HCmoSQZzl//jw0NDTg6Oio7FKICuA9yKKjo7FixQqUl5e3+ZpAIKCFNQkhz8QYQ3x8PBobGzFhwgS6ZjbA8T5q8c9//jNCQ0NRXFwMmUwm96AQI4QoIikpCQkJCWhpaVF2KUSJeA+y0tJSvP3227CwsOC7aUJIP3Tv3j1ERUWhoqJC2aUQJeE9yBYuXIiEhAS+myWE9GNVVVU4cuQIbty40ea6O+n/eL9G9t133yE0NBQXLlzAhAkTIBQK5b6+YcMGvksihPQDUqkUV69eRW5uLnx8fGBmZqbskghPeA+yvXv3IiYmBiKRCAkJCXIXaQUCAQUZIaRHysvLERUVBWdnZ0yZMgWamprKLon0MaWsEL1582a8//770NBQ2nJohJB+jDGGmzdvIj8/HzNnzsTgwYOVXRLpQ7wniVgsxqJFiyjECCF9rvXa2Z07d5RdCulDvKfJypUrERkZyXezhBA1k5+fj4aGBgB//AHc3TXJpFIpLly4gEuXLtFAkH6K91OLUqkUW7ZsQUxMDJydndsM9vj3v//Nd0mEEBWSnJyMTz/9FCdOnOBmu29oaMDf//53TJgwAXPnzoWdnZ3C75uRkYGGhgbMnDmTrpv1M7wHWXp6Ojfh561bt+S+1pd35//jH//AiRMnkJqaCi0tLTx+/LjP2iKEdM/hw4exaNEiMMbaLNnCGMOtW7dw69YtrF27Fi4uLgq/f25uLhISEjBz5kyaDaQf4T3I4uPj+W4SwB+nJkJDQzFt2jRs375dKTUQQjqWnJyMRYsWQSqVdrjuWOupwW3btuG9997r1pFZTk4O7OzsYG9v35NyiQoZMCMuNm3ahLfeegsTJkxQdimEkHZ89tln7R6JdeTkyZPdbuvps0FEvQ2YIOuO5uZm1NTUcI+6ujpll0RIv5Sfn4/jx493eb5VmUyGtLS0bg8AefToEcrKyrr1WqJ6KMg68cUXX8DIyIh7+Pr6KrskQvqlM2fOdPlIrBVjDJmZmd1uMzExUeE2iWpS6yDbuHEjBAJBp4+UlJRuv/8HH3yA6upq7nHu3LlerJ4Q0qq2tlbhe0sFAgGampq63WZJSQkePHjQ7dcT1aGUhTV7y5///GcsXry40326czG4lba2NrS1tbnn+vr63X4vQkjHDAwMFL7HizEGHR2dHrVbVFSEESNG9Og9iPKpdZCZmZnRxKCE9AOzZs2CQCBQ6FSfQCDAmDFjetTukCFDevR6ohrU+tSiIvLz85Gamor8/HxIpVKkpqYiNTWVBnAQogJsbGwQHBzc5RuVNTQ04OzsDFNT0263aW1tDQcHh26/nqiOARNkn3zyCSZPnozw8HDU1dVh8uTJmDx5co+uoRFCes/HH3/MXdvuijlz5nS7LWtrazz33HN0U3Q/MWCCbOfOndw9Kk8+/Pz8lF0aIQTA1KlTERkZCU1NzQ6PzDQ0NKChoYFXX32129e/J06ciICAAAwapNZXVsgTBkyQEUJU34IFC3D58mXMmTOnzdGSQCDAhAkT8N5773HT3ClCR0cHAQEBcHd3p9U3+hn6k4QQolKmTp2KY8eOIT8/H5MmTUJVVRV0dXXx8ccfd/uamLW1NXx9faGrq9vL1RJVQEFGCFFJNjY20NXVRVVVFbS0tLoVYkKhEB4eHhgzZgxdD+vHKMgIIf2SjY0Npk+fTvd/DgAUZISQfkVHRwdeXl4YOXIkHYUNEBRkhJB+w9raGn5+fhCJRMouhfCIgowQ0i+MHz8e06ZNo6OwAYjGoBJC1N7YsWMpxAYwCjJCiFobPnw4vLy8KMQGMAoyQojasrCwwHPPPUc3OA9wdI2MEKKWrKysEBAQAKFQqOxSiJJRkBFC1I6joyN8fHy6PFs+6d8oyAghamXixIlwc3Oja2KEQyeWCSFqY+zYsRRipA06IiOEqKyhQ4eiubkZurq6MDQ0hKenJ4UYaYOCjBCislJSUnD27FlkZ2dj/PjxdE2MtItOLRJCVJ5AIIC9vb2yyyAqioKMEKLyzM3Naf5E0iEKMkKIyrO2tlZ2CUSFUZARQlSelZWVsksgKoyCjBCi0gQCAczMzJRdBlFhFGSEEJVmZGRE01CRTlGQEUJUmrGxsbJLICqOgowQotIMDAyUXQJRcRRkhBCVZmJiouwSiIqjICOEqLRBg2gCItI5CjJCCCFqjYKMEEKIWqMgI4QQotYoyAghhKg1CjJCCCFqjYKMEEKIWqNxrWqsuLgYxcXFyi6DENLPWFpawtLSUtlldBkFmQIsLS0RHh6uEt/g5uZmLFmyBOfOnVN2KYSQfsbX1xcxMTHQ1tZWdildImCMMWUXQRRXU1MDIyMjnDt3Dvr6+souZ0Coq6uDr68v9TmPqM/519rn1dXVMDQ0VHY5XUJHZGpu0qRJavOfTd3V1NQAoD7nE/U5/1r7XJ3QYA9CCCFqjYKMEEKIWqMgU1Pa2toIDw9Xm4ux/QH1Of+oz/mnjn1Ogz0IIYSoNToiI4QQotYoyAghhKg1CjJCCCFqjYKMEEKIWqMgI/2aQCDo9LFq1apuv7ednR2+/fbbZ+63detW+Pn5wdDQEAKBAI8fP+52m+pA2X1eWVmJ9evXY/To0dDV1YWNjQ02bNiA6urqbrer6pTd5wDwpz/9Cfb29hCJRDA3N8e8efOQmZnZ7XYVQTN7kH7tyUmVIyMj8cknnyArK4vbJhKJ+ryGhoYGBAYGIjAwEB988EGft6dsyu7zoqIiFBUV4euvv4aTkxPy8vKwbt06FBUV4eDBg33atrIou88BwNXVFcuWLYONjQ0qKyuxceNG+Pv7Izc3F5qamn3bOCNkgNixYwczMjKS23bs2DHm4uLCtLW12YgRI9jGjRuZRCLhvh4eHs6sra2ZlpYWs7S0ZOvXr2eMMebr68sAyD2eJT4+ngFgVVVVvfmxVJqy+7zV77//zrS0tOTa6a9Upc9v3rzJALDs7Oxe+VydoSMyMmDFxMRg+fLl+M9//gNvb2/k5OTg1VdfBQCEh4fj4MGD+Oabb7B//36MGzcOJSUluHnzJgDg8OHDmDhxIl599VWsXbtWmR9DrSirz1snwB00aOD9ylNGn9fX12PHjh0YMWIErK2t++RzyenzqCRERTz9l6q3tzf7/PPP5faJiIhglpaWjDHG/vWvf7FRo0YxsVjc7vvZ2tqyb775psvt0xEZ/33OGGPl5eXMxsaGffjhhwq9Tl0ps8+///57pqenxwCwMWPG8HI0xhhjNNiDDFjXrl3D5s2boa+vzz3Wrl2L4uJiNDQ0IDQ0FI2NjRg5ciTWrl2LqKgotLS0KLtstcZ3n9fU1GDu3LlwcnJCeHh4L34S9cFnny9btgw3btzAuXPn4OjoiJdeeglNTU29/InaGnjH2YT8H5lMhk2bNmHBggVtvqajowNra2tkZWUhNjYWcXFxeP311/HVV1/h3LlzEAqFSqhY/fHZ57W1tQgMDIS+vj6ioqIG7PeMzz43MjKCkZERHB0d4eHhARMTE0RFRWHJkiW99XHaRUFGBiwXFxdkZWXBwcGhw31EIhFCQkIQEhKCN954A2PGjEF6ejpcXFygpaUFqVTKY8Xqj68+r6mpQUBAALS1tXHs2DHo6Oj05sdQK8r8f84YQ3Nzc3dL7zIKMjJgffLJJwgODoa1tTVCQ0OhoaGBtLQ0pKen47PPPsPOnTshlUrh7u4OXV1dREREQCQSwdbWFsAf99ecP38eixcvhra2NszMzNptp6SkBCUlJcjOzgYApKenw8DAADY2NjA1NeXt86oCPvq8trYW/v7+aGhowG+//YaamhpusUhzc/O+HwquYvjo8/v37yMyMhL+/v4wNzdHYWEhvvzyS4hEIsyZM6fvPyQvV+IIUQHtDUuOjo5mnp6eTCQSMUNDQ+bm5sa2bt3KGGMsKiqKubu7M0NDQ6anp8c8PDxYXFwc99rExETm7OzMtLW1Ox2WHB4e3mYIMwC2Y8eOvviYKkUZfd46qKa9R25ubl99VJWhjD4vLCxkQUFBbMiQIUwoFLLhw4ezpUuXsszMzD77nE+iZVwIIYSoNRq1SAghRK1RkBFCCFFrFGSEEELUGgUZIYQQtUZBRsgTEhISBsRSK6qE+px//a3PadQiIU8Qi8WorKyEhYUFBAKBsssZEKjP+dff+pyCjBBCiFqjU4ukX/Pz88P69evx5ptvwsTEBBYWFti6dSvq6+vx8ssvw8DAAPb29jh16hSAtqdcdu7cCWNjY8TExGDs2LHQ19dHYGCg3EKGfn5+ePPNN+XanT9/vtyqvD/88AMcHR2ho6MDCwsLLFy4sK8/utJQn/NvoPc5BRnp93bt2gUzMzMkJydj/fr1eO211xAaGgpPT09cv34dAQEBCAsLQ0NDQ7uvb2howNdff42IiAicP38e+fn5+Otf/9rl9lNSUrBhwwZs3rwZWVlZiI6Oho+PT299PJVEfc6/Ad3nvMwfQoiS+Pr6sunTp3PPW1pamJ6eHgsLC+O2FRcXMwAsMTGxzZphO3bsaLPK7ffff88sLCzk2vjLX/4i1+68efPYypUrGWOMHTp0iBkaGrKampre/4AqiPqcfwO9z+mIjPR7zs7O3L81NTUxePBgTJgwgdtmYWEBACgtLW339bq6urC3t+eeW1padrhve5577jnY2tpi5MiRCAsLw549ezr8q7i/oD7n30Ducwoy0u89vaaSQCCQ29Y6aksmk3X59eyJMVIaGhpyzwFAIpFw/zYwMMD169exb98+WFpa4pNPPsHEiRP7zdDn9lCf828g9zkFGSE9ZG5uLndRXCqV4tatW3L7DBo0CLNnz8aWLVuQlpaGBw8e4OzZs3yX2m9Qn/NPlfuc1iMjpIdmzpyJt99+GydOnIC9vT2++eYbub9Cjx8/jvv378PHxwcmJiY4efIkZDIZRo8erbyi1Rz1Of9Uuc8pyAjpodWrV+PmzZtYsWIFBg0ahLfeegszZszgvm5sbIzDhw9j48aNaGpqgqOjI/bt24dx48YpsWr1Rn3OP1Xuc7ohmhBCiFqja2SEEELUGgUZIYQQtUZBRgghRK1RkBFCCFFrFGSE8KS/rQFFiKqgICNqqaSkBOvXr8fIkSOhra0Na2trPP/88zhz5kyvttPejN99aevWrfDz84OhoaHahp5AIOj08eRs6Yqys7PDt99++8z9+kM/KkLZfV5ZWYn169dj9OjR0NXVhY2NDTZs2IDq6uput6sIuo+MqJ0HDx7Ay8sLxsbG2LJlC5ydnSGRSBATE4M33ngDmZmZvNbDGINUKsWgQT3/cWpoaEBgYCACAwPxwQcf9EJ1/Hty9ofIyEh88sknyMrK4raJRKI+r6E/9KMilN3nRUVFKCoqwtdffw0nJyfk5eVh3bp1KCoqwsGDB/u0bQA0+z1RP0FBQWzYsGGsrq6uzddaZ/NmjLG8vDwWEhLC9PT0mIGBAQsNDWUlJSXc18PDw9nEiRPZ7t27ma2tLTM0NGSLFi3iZu9euXIlAyD3yM3N5WYOj46OZq6urkwoFLKzZ8+ypqYmtn79emZubs60tbWZl5cXS05O5tp7esbxziiyryrbsWMHMzIyktt27Ngx5uLiwrS1tdmIESPYxo0bmUQi4b4eHh7OrK2tmZaWFrO0tGTr169njP0x+/rT349n6S/9qAhl93mr33//nWlpacm101coyIhaqaioYAKBgH3++eed7ieTydjkyZPZ9OnTWUpKCktKSmIuLi7M19eX2yc8PJzp6+uzBQsWsPT0dHb+/Hk2dOhQ9ve//50xxtjjx4/ZtGnT2Nq1a1lxcTErLi5mLS0t3C9HZ2dndvr0aZadnc3Ky8vZhg0bmJWVFTt58iTLyMhgK1euZCYmJqyiooIxRkHGGGPR0dHM0NCQ7dy5k+Xk5LDTp08zOzs7tnHjRsYYYwcOHGCGhobs5MmTLC8vj125coVt3bqVMfbH93748OFs8+bN3PfjWfpLPypC2X3eatu2bczMzKxXP1tHKMiIWrly5QoDwA4fPtzpfqdPn2aamposPz+f25aRkcEAcEdJ4eHhTFdXV279pHfffZe5u7tzz9tbg6n1l+ORI0e4bXV1dUwoFLI9e/Zw28RiMbOysmJbtmyRe91ADjJvb+82f4REREQwS0tLxhhj//rXv9ioUaOYWCxu9/1sbW3ZN9980+X2+0s/KkLZfc4YY+Xl5czGxoZ9+OGHCr2uu2iwB1Er7P9mVGtdkqIjd+7cgbW1NaytrbltTk5OMDY2xp07d7htdnZ2MDAw4J4rsgbTlClTuH/n5ORAIpHAy8uL2yYUCuHm5ibX3kB37do1bN68Gfr6+txj7dq1KC4uRkNDA0JDQ9HY2IiRI0di7dq1iIqKQktLi7LLVmt893lNTQ3mzp0LJycnhIeH9+In6RgFGVErjo6OEAgEzwwHxli7Yff09vbWYOpovaan6enpyb1v6+u7UsdAJZPJsGnTJqSmpnKP9PR03Lt3Dzo6OrC2tkZWVha+//57iEQivP766/Dx8ZFb94oohs8+r62tRWBgIPT19REVFdXm56uvUJARtWJqaoqAgAB8//33qK+vb/P11mHWTk5OyM/PR0FBAfe127dvo7q6GmPHju1ye1paWpBKpc/cz8HBAVpaWrh48SK3TSKRICUlRaH2+jsXFxdkZWXBwcGhzUND449fRyKRCCEhIfjPf/6DhIQEJCYmIj09HUDXvx/kv/jq85qaGvj7+0NLSwvHjh2Djo5On36uJ9Hwe6J2fvjhB3h6esLNzQ2bN2+Gs7MzWlpaEBsbix9//BF37tzB7Nmz4ezsjGXLluHbb79FS0sLXn/9dfj6+sqdEnwWOzs7XLlyBQ8ePIC+vj5MTU3b3U9PTw+vvfYa3n33XZiamsLGxgZbtmxBQ0MD1qxZ0+X2SkpKUFJSguzsbABAeno6DAwMYGNj02Hb6uSTTz5BcHAwrK2tERoaCg0NDaSlpSE9PR2fffYZdu7cCalUCnd3d+jq6iIiIgIikQi2trYA/vh+nD9/HosXL4a2tjbMzMzabae/96Mi+Ojz2tpa+Pv7o6GhAb/99htqampQU1MD4I8FOTU1Nfv2Q/JyJY6QXlZUVMTeeOMNZmtry7S0tNiwYcNYSEgIi4+P5/bp6vD7J33zzTfM1taWe56VlcU8PDyYSCRqM/z+6QEEjY2NbP369czMzKzbw+/Dw8PbDHcGwHbs2NGNXlK+9oaCR0dHM09PTyYSiZihoSFzc3PjRslFRUUxd3d3ZmhoyPT09JiHhweLi4vjXpuYmMicnZ2ZtrZ2p0PB+1s/KkIZfd76f7u9R25ubl99VA6tR0YIIUSt0TUyQgghao2CjBBCiFqjICOEEKLWKMgIIYSoNQoyQgghao2CjBBCiFqjICOEEKLWKMgIIYSoNQoyQgghao2CjBBCiFqjICOEEKLWKMgIIYSotf8P05q2e/6lf8AAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_rm_sequential = dabest.load(df, id_col = \"ID\", paired = \"sequential\",\n", + " idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\"))\n", + "fig8 = my_rm_sequential.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d22bdc4c", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class PermutationTest:\n", + " \"\"\"\n", + " A class to compute and report permutation tests.\n", + " \n", + " Parameters\n", + " ----------\n", + " control : array-like\n", + " test : array-like\n", + " These should be numerical iterables.\n", + " effect_size : string.\n", + " Any one of the following are accepted inputs:\n", + " 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", + " is_paired : string, default None\n", + " permutation_count : int, default 10000\n", + " The number of permutations (reshuffles) to perform.\n", + " random_seed : int, default 12345\n", + " `random_seed` is used to seed the random number generator during\n", + " bootstrap resampling. This ensures that the generated permutations\n", + " are replicable.\n", + " \n", + " Returns\n", + " -------\n", + " A :py:class:`PermutationTest` object:\n", + " `difference`:float\n", + " The effect size of the difference between the control and the test.\n", + " `effect_size`:string\n", + " The type of effect size reported.\n", + " \n", + " \n", + " \"\"\"\n", + " \n", + " def __init__(self, control:np.array,\n", + " test:np.array, # These should be numerical iterables.\n", + " effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", + " is_paired:str=None,\n", + " permutation_count:int=5000, # The number of permutations (reshuffles) to perform.\n", + " random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable.\n", + " **kwargs):\n", + " \n", + " import numpy as np\n", + " from numpy.random import PCG64, RandomState\n", + " from ._stats_tools.effsize import two_group_difference\n", + " from ._stats_tools.confint_2group_diff import calculate_group_var\n", + " \n", + "\n", + " self.__permutation_count = permutation_count\n", + "\n", + " # Run Sanity Check.\n", + " if is_paired and len(control) != len(test):\n", + " raise ValueError(\"The two arrays do not have the same length.\")\n", + "\n", + " # Initialise random number generator.\n", + " # rng = np.random.default_rng(seed=random_seed)\n", + " rng = RandomState(PCG64(random_seed))\n", + "\n", + " # Set required constants and variables\n", + " control = np.array(control)\n", + " test = np.array(test)\n", + "\n", + " control_sample = control.copy()\n", + " test_sample = test.copy()\n", + "\n", + " BAG = np.array([*control, *test])\n", + " CONTROL_LEN = int(len(control))\n", + " EXTREME_COUNT = 0.\n", + " THRESHOLD = np.abs(two_group_difference(control, test, \n", + " is_paired, effect_size))\n", + " self.__permutations = []\n", + " self.__permutations_var = []\n", + "\n", + " for i in range(int(permutation_count)):\n", + " \n", + " if is_paired:\n", + " # Select which control-test pairs to swap.\n", + " random_idx = rng.choice(CONTROL_LEN,\n", + " rng.randint(0, CONTROL_LEN+1),\n", + " replace=False)\n", + "\n", + " # Perform swap.\n", + " for i in random_idx:\n", + " _placeholder = control_sample[i]\n", + " control_sample[i] = test_sample[i]\n", + " test_sample[i] = _placeholder\n", + " \n", + " else:\n", + " # Shuffle the bag and assign to control and test groups.\n", + " # NB. rng.shuffle didn't produce replicable results...\n", + " shuffled = rng.permutation(BAG) \n", + " control_sample = shuffled[:CONTROL_LEN]\n", + " test_sample = shuffled[CONTROL_LEN:]\n", + "\n", + "\n", + " es = two_group_difference(control_sample, test_sample, \n", + " False, effect_size)\n", + " \n", + " var = calculate_group_var(np.var(control_sample, ddof=1), \n", + " CONTROL_LEN, \n", + " np.var(test_sample, ddof=1), \n", + " len(test_sample))\n", + " self.__permutations.append(es)\n", + " self.__permutations_var.append(var)\n", + "\n", + " if np.abs(es) > THRESHOLD:\n", + " EXTREME_COUNT += 1.\n", + "\n", + " self.__permutations = np.array(self.__permutations)\n", + " self.__permutations_var = np.array(self.__permutations_var)\n", + "\n", + " self.pvalue = EXTREME_COUNT / permutation_count\n", + "\n", + "\n", + " def __repr__(self):\n", + " return(\"{} permutations were taken. The p-value is {}.\".format(self.permutation_count, \n", + " self.pvalue))\n", + "\n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__permutation_count\n", + "\n", + "\n", + " @property\n", + " def permutations(self):\n", + " \"\"\"\n", + " The effect sizes of all the permutations in a list.\n", + " \"\"\"\n", + " return self.__permutations\n", + "\n", + " \n", + " @property\n", + " def permutations_var(self):\n", + " \"\"\"\n", + " The experiment group variance of all the permutations in a list.\n", + " \"\"\"\n", + " return self.__permutations_var\n" + ] + }, + { + "cell_type": "markdown", + "id": "3214e42a", + "metadata": {}, + "source": [ + "**Notes**:\n", + " \n", + "The basic concept of permutation tests is the same as that behind bootstrapping.\n", + "In an \"exact\" permutation test, all possible resuffles of the control and test \n", + "labels are performed, and the proportion of effect sizes that equal or exceed \n", + "the observed effect size is computed. This is the probability, under the null \n", + "hypothesis of zero difference between test and control groups, of observing the\n", + "effect size: the p-value of the Student's t-test.\n", + "\n", + "Exact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of $20!$ or $2.43 \\times {10}^{18}$ reshuffles.\n", + "Therefore, in practice, \"approximate\" permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed.\n", + "\n", + "More information can be found [here](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests).\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc181ae2", + "metadata": {}, + "source": [ + "#### Example: permutation test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fc2c6b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5000 permutations were taken. The p-value is 0.0758." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "perm_test = dabest.PermutationTest(control, test, \n", + " effect_size=\"mean_diff\", \n", + " is_paired=None)\n", + "perm_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07a84d5f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/confint_1group.ipynb b/nbs/API/confint_1group.ipynb new file mode 100644 index 00000000..1e547098 --- /dev/null +++ b/nbs/API/confint_1group.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6e9be445", + "metadata": {}, + "source": [ + "# confint_1group\n", + "\n", + "> A range of functions to compute bootstraps for a single sample.\n", + "\n", + "- order: 7" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80646fa2", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _stats_tools/confint_1group" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65a5b76c", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ef2b7e1", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9181f236", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bac09924", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def create_bootstrap_indexes(array, resamples=5000, random_seed=12345):\n", + " \"\"\"Given an array-like, returns a generator of bootstrap indexes\n", + " to be used for resampling.\n", + " \"\"\"\n", + " import numpy as np\n", + " from numpy.random import PCG64, RandomState\n", + " rng = RandomState(PCG64(random_seed))\n", + " \n", + " indexes = range(0, len(array))\n", + "\n", + " out = (rng.choice(indexes, len(indexes), replace=True)\n", + " for i in range(0, resamples))\n", + " \n", + " # Reset RNG\n", + " # rng = RandomState(MT19937())\n", + " return out\n", + "\n", + "\n", + "\n", + "def compute_1group_jackknife(x, func, *args, **kwargs):\n", + " \"\"\"\n", + " Returns the jackknife bootstraps for func(x).\n", + " \"\"\"\n", + " from . import confint_2group_diff as ci_2g\n", + " jackknives = [i for i in ci_2g.create_jackknife_indexes(x)]\n", + " out = [func(x[j], *args, **kwargs) for j in jackknives]\n", + " del jackknives # memory management.\n", + " return out\n", + "\n", + "\n", + "\n", + "def compute_1group_acceleration(jack_dist):\n", + " from . import confint_2group_diff as ci_2g\n", + " return ci_2g._calc_accel(jack_dist)\n", + "\n", + "\n", + "\n", + "def compute_1group_bootstraps(x, func, resamples=5000, random_seed=12345,\n", + " *args, **kwargs):\n", + " \"\"\"Bootstraps func(x), with the number of specified resamples.\"\"\"\n", + "\n", + " import numpy as np\n", + " \n", + " # Create bootstrap indexes.\n", + " boot_indexes = create_bootstrap_indexes(x, resamples=resamples,\n", + " random_seed=random_seed)\n", + "\n", + " out = [func(x[b], *args, **kwargs) for b in boot_indexes]\n", + " \n", + " del boot_indexes\n", + " \n", + " return out\n", + "\n", + "\n", + "\n", + "def compute_1group_bias_correction(x, bootstraps, func, *args, **kwargs):\n", + " from scipy.stats import norm\n", + " metric = func(x, *args, **kwargs)\n", + " prop_boots_less_than_metric = sum(bootstraps < metric) / len(bootstraps)\n", + "\n", + " return norm.ppf(prop_boots_less_than_metric)\n", + "\n", + "\n", + "\n", + "def summary_ci_1group(x:np.array,# An numerical iterable.\n", + " func, #The function to be applied to x.\n", + " resamples:int=5000, #The number of bootstrap resamples to be taken of func(x).\n", + " alpha:float=0.05, #Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n", + " random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.\n", + " sort_bootstraps:bool=True, \n", + " *args, **kwargs):\n", + " \"\"\"\n", + " Given an array-like x, returns func(x), and a bootstrap confidence\n", + " interval of func(x).\n", + "\n", + " Returns\n", + " -------\n", + " A dictionary with the following five keys:\n", + " `summary`: float.\n", + " The outcome of func(x).\n", + " `func`: function.\n", + " The function applied to x.\n", + " `bca_ci_low`: float\n", + " `bca_ci_high`: float.\n", + " The bias-corrected and accelerated confidence interval, for the\n", + " given alpha.\n", + " `bootstraps`: array.\n", + " The bootstraps used to generate the confidence interval.\n", + " These will be sorted in ascending order if `sort_bootstraps`\n", + " was True.\n", + "\n", + " \"\"\"\n", + " from . import confint_2group_diff as ci2g\n", + " from numpy import sort as npsort\n", + "\n", + " boots = compute_1group_bootstraps(x, func, resamples=resamples,\n", + " random_seed=random_seed,\n", + " *args, **kwargs)\n", + " bias = compute_1group_bias_correction(x, boots, func)\n", + "\n", + " jk = compute_1group_jackknife(x, func, *args, **kwargs)\n", + " accel = compute_1group_acceleration(jk)\n", + " del jk\n", + "\n", + " ci_idx = ci2g.compute_interval_limits(bias, accel, resamples, alpha)\n", + "\n", + " boots_sorted = npsort(boots)\n", + "\n", + " low = boots_sorted[ci_idx[0]]\n", + " high = boots_sorted[ci_idx[1]]\n", + "\n", + " if sort_bootstraps:\n", + " B = boots_sorted\n", + " else:\n", + " B = boots\n", + " del boots\n", + " del boots_sorted\n", + "\n", + " out = {'summary': func(x), 'func': func,\n", + " 'bca_ci_low': low, 'bca_ci_high': high,\n", + " 'bootstraps': B}\n", + "\n", + " del B\n", + " return out\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1bdd2b6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/confint_2group_diff.ipynb b/nbs/API/confint_2group_diff.ipynb new file mode 100644 index 00000000..2d482f3b --- /dev/null +++ b/nbs/API/confint_2group_diff.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebadf154", + "metadata": {}, + "source": [ + "# confint_2group_diff\n", + "\n", + "> A range of functions to compute bootstraps for the mean difference \n", + "between two groups.\n", + "\n", + "- order: 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77004140", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _stats_tools/confint_2group_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f96815", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fd5572a", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa733643", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cf9b1fc", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def create_jackknife_indexes(data):\n", + " \"\"\"\n", + " Given an array-like, creates a jackknife bootstrap.\n", + "\n", + " For a given set of data Y, the jackknife bootstrap sample J[i]\n", + " is defined as the data set Y with the ith data point deleted.\n", + "\n", + " Keywords\n", + " --------\n", + " data: array-like\n", + "\n", + " Returns\n", + " -------\n", + " Generator that yields all jackknife bootstrap samples.\n", + " \"\"\"\n", + " from numpy import arange, delete\n", + "\n", + " index_range = arange(0, len(data))\n", + " return (delete(index_range, i) for i in index_range)\n", + "\n", + "\n", + "\n", + "def create_repeated_indexes(data):\n", + " \"\"\"\n", + " Convenience function. Given an array-like with length N,\n", + " returns a generator that yields N indexes [0, 1, ..., N].\n", + " \"\"\"\n", + " from numpy import arange\n", + "\n", + " index_range = arange(0, len(data))\n", + " return (index_range for i in index_range)\n", + "\n", + "\n", + "\n", + "def _create_two_group_jackknife_indexes(x0, x1, is_paired):\n", + " \"\"\"Creates the jackknife bootstrap for 2 groups.\"\"\"\n", + "\n", + " if is_paired and len(x0) == len(x1):\n", + " out = list(zip([j for j in create_jackknife_indexes(x0)],\n", + " [i for i in create_jackknife_indexes(x1)]\n", + " )\n", + " )\n", + " else:\n", + " jackknife_c = list(zip([j for j in create_jackknife_indexes(x0)],\n", + " [i for i in create_repeated_indexes(x1)]\n", + " )\n", + " )\n", + "\n", + " jackknife_t = list(zip([i for i in create_repeated_indexes(x0)],\n", + " [j for j in create_jackknife_indexes(x1)]\n", + " )\n", + " )\n", + " out = jackknife_c + jackknife_t\n", + " del jackknife_c\n", + " del jackknife_t\n", + "\n", + " return out\n", + "\n", + "\n", + "\n", + "def compute_meandiff_jackknife(x0, x1, is_paired, effect_size):\n", + " \"\"\"\n", + " Given two arrays, returns the jackknife for their effect size.\n", + " \"\"\"\n", + " from . import effsize as __es\n", + "\n", + " jackknives = _create_two_group_jackknife_indexes(x0, x1, is_paired)\n", + "\n", + " out = []\n", + "\n", + " for j in jackknives:\n", + " x0_shuffled = x0[j[0]]\n", + " x1_shuffled = x1[j[1]]\n", + "\n", + " es = __es.two_group_difference(x0_shuffled, x1_shuffled,\n", + " is_paired, effect_size)\n", + " out.append(es)\n", + "\n", + " return out\n", + "\n", + "\n", + "\n", + "def _calc_accel(jack_dist):\n", + " from numpy import mean as npmean\n", + " from numpy import sum as npsum\n", + " from numpy import errstate\n", + "\n", + " jack_mean = npmean(jack_dist)\n", + "\n", + " numer = npsum((jack_mean - jack_dist)**3)\n", + " denom = 6.0 * (npsum((jack_mean - jack_dist)**2) ** 1.5)\n", + "\n", + " with errstate(invalid='ignore'):\n", + " # does not raise warning if invalid division encountered.\n", + " return numer / denom\n", + "\n", + "\n", + "def compute_bootstrapped_diff(x0, x1, is_paired, effect_size,\n", + " resamples=5000, random_seed=12345):\n", + " \"\"\"Bootstraps the effect_size for 2 groups.\"\"\"\n", + " \n", + " from . import effsize as __es\n", + " import numpy as np\n", + " from numpy.random import PCG64, RandomState\n", + " \n", + " # rng = RandomState(default_rng(random_seed))\n", + " rng = RandomState(PCG64(random_seed))\n", + "\n", + " out = np.repeat(np.nan, resamples)\n", + " x0_len = len(x0)\n", + " x1_len = len(x1)\n", + " \n", + " for i in range(int(resamples)):\n", + " \n", + " if is_paired:\n", + " if x0_len != x1_len:\n", + " raise ValueError(\"The two arrays do not have the same length.\")\n", + " random_idx = rng.choice(x0_len, x0_len, replace=True)\n", + " x0_sample = x0[random_idx]\n", + " x1_sample = x1[random_idx]\n", + " else:\n", + " x0_sample = rng.choice(x0, x0_len, replace=True)\n", + " x1_sample = rng.choice(x1, x1_len, replace=True)\n", + " \n", + " out[i] = __es.two_group_difference(x0_sample, x1_sample,\n", + " is_paired, effect_size)\n", + " \n", + " # check whether there are any infinities in the bootstrap,\n", + " # which likely indicates the sample sizes are too small as\n", + " # the computation of Cohen's d and Hedges' g necessitated \n", + " # a division by zero.\n", + " # Added in v0.2.6.\n", + " \n", + " # num_infinities = len(out[np.isinf(out)])\n", + " # print(num_infinities)\n", + " # if num_infinities > 0:\n", + " # warn_msg = \"There are {} bootstraps that are not defined. \"\\\n", + " # \"This is likely due to smaple sample sizes. \"\\\n", + " # \"The values in a bootstrap for a group will be more likely \"\\\n", + " # \"to be all equal, with a resulting variance of zero. \"\\\n", + " # \"The computation of Cohen's d and Hedges' g will therefore \"\\\n", + " # \"involved a division by zero. \"\n", + " # warnings.warn(warn_msg.format(num_infinities), category=\"UserWarning\")\n", + " \n", + " return out\n", + "\n", + "\n", + "\n", + "\n", + "def compute_meandiff_bias_correction(bootstraps, #An numerical iterable, comprising bootstrap resamples of the effect size.\n", + " effsize # The effect size for the original sample.\n", + " ): #The bias correction value for the given bootstraps and effect size.\n", + " \"\"\"\n", + " Computes the bias correction required for the BCa method\n", + " of confidence interval construction.\n", + "\n", + " Returns\n", + " -------\n", + " bias: numeric\n", + " The bias correction value for the given bootstraps\n", + " and effect size.\n", + "\n", + " \"\"\"\n", + " from scipy.stats import norm\n", + " from numpy import array\n", + "\n", + " B = array(bootstraps)\n", + " prop_less_than_es = sum(B < effsize) / len(B)\n", + "\n", + " return norm.ppf(prop_less_than_es)\n", + "\n", + "\n", + "\n", + "def _compute_alpha_from_ci(ci):\n", + " if ci < 0 or ci > 100:\n", + " raise ValueError(\"`ci` must be a number between 0 and 100.\")\n", + "\n", + " return (100. - ci) / 100.\n", + "\n", + "\n", + "\n", + "def _compute_quantile(z, bias, acceleration):\n", + " numer = bias + z\n", + " denom = 1 - (acceleration * numer)\n", + "\n", + " return bias + (numer / denom)\n", + "\n", + "\n", + "\n", + "def compute_interval_limits(bias, acceleration, n_boots, ci=95):\n", + " \"\"\"\n", + " Returns the indexes of the interval limits for a given bootstrap.\n", + "\n", + " Supply the bias, acceleration factor, and number of bootstraps.\n", + " \"\"\"\n", + " from scipy.stats import norm\n", + " from numpy import isnan, nan\n", + "\n", + " alpha = _compute_alpha_from_ci(ci)\n", + "\n", + " alpha_low = alpha / 2\n", + " alpha_high = 1 - (alpha / 2)\n", + "\n", + " z_low = norm.ppf(alpha_low)\n", + " z_high = norm.ppf(alpha_high)\n", + "\n", + " kws = {'bias': bias, 'acceleration': acceleration}\n", + " low = _compute_quantile(z_low, **kws)\n", + " high = _compute_quantile(z_high, **kws)\n", + "\n", + " if isnan(low) or isnan(high):\n", + " return low, high\n", + "\n", + " else:\n", + " low = int(norm.cdf(low) * n_boots)\n", + " high = int(norm.cdf(high) * n_boots)\n", + " return low, high\n", + "\n", + "\n", + "def calculate_group_var(control_var, control_N,test_var, test_N):\n", + " return control_var/control_N + test_var/test_N\n", + "\n", + "\n", + "def calculate_weighted_delta(group_var, differences, resamples):\n", + " '''\n", + " Compute the weighted deltas.\n", + " '''\n", + " import numpy as np\n", + "\n", + " weight = 1/group_var\n", + " denom = np.sum(weight)\n", + " num = np.sum(weight[i] * differences[i] for i in range(0, len(weight)))\n", + "\n", + " return num/denom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87e0c164", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/effsize.ipynb b/nbs/API/effsize.ipynb new file mode 100644 index 00000000..bbce4c26 --- /dev/null +++ b/nbs/API/effsize.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8a807e60", + "metadata": {}, + "source": [ + "# effsize\n", + "\n", + "> A range of functions to compute various effect sizes.\n", + "\n", + "- order: 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e1432b", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _stats_tools/effsize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6673b99", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa0a0c20", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d92d449", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "from __future__ import annotations\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0547f8a7", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.\n", + " test:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.\n", + " is_paired=None, #If not None, returns the paired Cohen's d\n", + " effect_size:str=\"mean_diff\" # Any one of the following effect sizes: [\"mean_diff\", \"median_diff\", \"cohens_d\", \"hedges_g\", \"cliffs_delta\"]\n", + " )->float: # The desired effect size.\n", + " \"\"\"\n", + " Computes the following metrics for control and test:\n", + " \n", + " - Unstandardized mean difference\n", + " - Standardized mean differences (paired or unpaired)\n", + " * Cohen's d\n", + " * Hedges' g\n", + " - Median difference\n", + " - Cliff's Delta\n", + " - Cohen's h (distance between two proportions)\n", + "\n", + " See the Wikipedia entry [here](https://bit.ly/2LzWokf)\n", + " \n", + " `effect_size`:\n", + " \n", + " mean_diff: This is simply the mean of `control` subtracted from\n", + " the mean of `test`.\n", + "\n", + " cohens_d: This is the mean of control subtracted from the\n", + " mean of test, divided by the pooled standard deviation\n", + " of control and test. The pooled SD is the square as:\n", + "\n", + "\n", + " (n1 - 1) * var(control) + (n2 - 1) * var(test)\n", + " sqrt ( ------------------------------------------- )\n", + " (n1 + n2 - 2)\n", + "\n", + " where n1 and n2 are the sizes of control and test\n", + " respectively.\n", + "\n", + " hedges_g: This is Cohen's d corrected for bias via multiplication\n", + " with the following correction factor:\n", + "\n", + " gamma(n/2)\n", + " J(n) = ------------------------------\n", + " sqrt(n/2) * gamma((n - 1) / 2)\n", + "\n", + " where n = (n1 + n2 - 2).\n", + "\n", + " median_diff: This is the median of `control` subtracted from the\n", + " median of `test`.\n", + "\n", + " \"\"\"\n", + " import numpy as np\n", + " import warnings\n", + "\n", + " if effect_size == \"mean_diff\":\n", + " return func_difference(control, test, np.mean, is_paired)\n", + "\n", + " elif effect_size == \"median_diff\":\n", + " mes1 = \"Using median as the statistic in bootstrapping may \" + \\\n", + " \"result in a biased estimate and cause problems with \" + \\\n", + " \"BCa confidence intervals. Consider using a different statistic, such as the mean.\\n\"\n", + " mes2 = \"When plotting, please consider using percetile confidence intervals \" + \\\n", + " \"by specifying `ci_type='percentile'`. For detailed information, \" + \\\n", + " \"refer to https://github.com/ACCLAB/DABEST-python/issues/129 \\n\"\n", + " warnings.warn(message=mes1+mes2, category=UserWarning)\n", + " return func_difference(control, test, np.median, is_paired)\n", + "\n", + " elif effect_size == \"cohens_d\":\n", + " return cohens_d(control, test, is_paired)\n", + "\n", + " elif effect_size == \"cohens_h\":\n", + " return cohens_h(control, test)\n", + "\n", + " elif effect_size == \"hedges_g\":\n", + " return hedges_g(control, test, is_paired)\n", + "\n", + " elif effect_size == \"cliffs_delta\":\n", + " if is_paired:\n", + " err1 = \"`is_paired` is not None; therefore Cliff's delta is not defined.\"\n", + " raise ValueError(err1)\n", + " else:\n", + " return cliffs_delta(control, test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c93f36d4", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically discarded.\n", + " test:list|tuple|np.ndarray, # NaNs are automatically discarded.\n", + " func, # Summary function to apply.\n", + " is_paired:str # If not None, computes func(test - control). If None, computes func(test) - func(control).\n", + " )->float:\n", + " \"\"\"\n", + " \n", + " Applies func to `control` and `test`, and then returns the difference.\n", + " \n", + " \"\"\"\n", + " import numpy as np\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " if control.__class__ != np.ndarray:\n", + " control = np.array(control)\n", + " if test.__class__ != np.ndarray:\n", + " test = np.array(test)\n", + "\n", + " if is_paired:\n", + " if len(control) != len(test):\n", + " err = \"The two arrays supplied do not have the same length.\"\n", + " raise ValueError(err)\n", + "\n", + " control_nan = np.where(np.isnan(control))[0]\n", + " test_nan = np.where(np.isnan(test))[0]\n", + "\n", + " indexes_to_drop = np.unique(np.concatenate([control_nan,\n", + " test_nan]))\n", + "\n", + " good_indexes = [i for i in range(0, len(control))\n", + " if i not in indexes_to_drop]\n", + "\n", + " control = control[good_indexes]\n", + " test = test[good_indexes]\n", + "\n", + " return func(test - control)\n", + "\n", + " else:\n", + " control = control[~np.isnan(control)]\n", + " test = test[~np.isnan(test)]\n", + " return func(test) - func(control)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6dd20e4", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def cohens_d(control:list|tuple|np.ndarray,\n", + " test:list|tuple|np.ndarray,\n", + " is_paired:str=None # If not None, the paired Cohen's d is returned.\n", + " )->float:\n", + " \"\"\"\n", + " Computes Cohen's d for test v.s. control.\n", + " See [here](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d)\n", + "\n", + " If `is_paired` is None, returns:\n", + " \n", + " $$\n", + " \\\\frac{\\\\bar{X}_2 - \\\\bar{X}_1}{s_{pooled}}\n", + " $$\n", + " \n", + " where\n", + " \n", + " $$\n", + " s_{pooled} = \\\\sqrt{\\\\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}}\n", + " $$\n", + " \n", + " If `is_paired` is not None, returns:\n", + " \n", + " $$\n", + " \\\\frac{\\\\bar{X}_2 - \\\\bar{X}_1}{s_{avg}}\n", + " $$\n", + " \n", + " where\n", + " \n", + " $$\n", + " s_{avg} = \\\\sqrt{\\\\frac{s_1^2 + s_2^2}{2}}\n", + " $$\n", + " \n", + " `Notes`:\n", + "\n", + " - The sample variance (and standard deviation) uses N-1 degrees of freedoms.\n", + " This is an application of Bessel's correction, and yields the unbiased\n", + " sample variance.\n", + "\n", + " `References`:\n", + " \n", + " - https://en.wikipedia.org/wiki/Bessel%27s_correction\n", + " - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation\n", + " \"\"\"\n", + " import numpy as np\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " if control.__class__ != np.ndarray:\n", + " control = np.array(control)\n", + " if test.__class__ != np.ndarray:\n", + " test = np.array(test)\n", + " control = control[~np.isnan(control)]\n", + " test = test[~np.isnan(test)]\n", + "\n", + " pooled_sd, average_sd = _compute_standardizers(control, test)\n", + " # pooled SD is used for Cohen's d of two independant groups.\n", + " # average SD is used for Cohen's d of two paired groups\n", + " # (aka repeated measures).\n", + " # NOT IMPLEMENTED YET: Correlation adjusted SD is used for Cohen's d of\n", + " # two paired groups but accounting for the correlation between\n", + " # the two groups.\n", + "\n", + " if is_paired:\n", + " # Check control and test are same length.\n", + " if len(control) != len(test):\n", + " raise ValueError(\"`control` and `test` are not the same length.\")\n", + " # assume the two arrays are ordered already.\n", + " delta = test - control\n", + " M = np.mean(delta)\n", + " divisor = average_sd\n", + "\n", + " else:\n", + " M = np.mean(test) - np.mean(control)\n", + " divisor = pooled_sd\n", + " \n", + " return M / divisor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93688770", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def cohens_h(control:list|tuple|np.ndarray, \n", + " test:list|tuple|np.ndarray\n", + " )->float:\n", + " '''\n", + " Computes Cohen's h for test v.s. control.\n", + " See [here](https://en.wikipedia.org/wiki/Cohen%27s_h for reference.)\n", + " \n", + " `Notes`:\n", + " \n", + " - Assuming the input data type is binary, i.e. a series of 0s and 1s,\n", + " and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: \"Smoker\", 0: \"Non-smoker\"}\n", + " '''\n", + "\n", + " import numpy as np\n", + " np.seterr(divide='ignore', invalid='ignore')\n", + " import pandas as pd\n", + "\n", + " # Check whether dataframe contains only 0s and 1s.\n", + " if np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False:\n", + " raise ValueError(\"Input data must be binary.\")\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " # Aligned with cohens_d calculation.\n", + " if control.__class__ != np.ndarray:\n", + " control = np.array(control)\n", + " if test.__class__ != np.ndarray:\n", + " test = np.array(test)\n", + " control = control[~np.isnan(control)]\n", + " test = test[~np.isnan(test)]\n", + "\n", + " prop_control = sum(control)/len(control)\n", + " prop_test = sum(test)/len(test)\n", + "\n", + " # Arcsine transformation\n", + " phi_control = 2 * np.arcsin(np.sqrt(prop_control))\n", + " phi_test = 2 * np.arcsin(np.sqrt(prop_test))\n", + "\n", + " return phi_test - phi_control\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcd77c32", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def hedges_g(control:list|tuple|np.ndarray, \n", + " test:list|tuple|np.ndarray, \n", + " is_paired:str=None)->float:\n", + " \"\"\"\n", + " Computes Hedges' g for for test v.s. control.\n", + " It first computes Cohen's d, then calulates a correction factor based on\n", + " the total degress of freedom using the gamma function.\n", + "\n", + " See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g)\n", + "\n", + " \"\"\"\n", + " import numpy as np\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " if control.__class__ != np.ndarray:\n", + " control = np.array(control)\n", + " if test.__class__ != np.ndarray:\n", + " test = np.array(test)\n", + " control = control[~np.isnan(control)]\n", + " test = test[~np.isnan(test)]\n", + "\n", + " d = cohens_d(control, test, is_paired)\n", + " len_c = len(control)\n", + " len_t = len(test)\n", + " correction_factor = _compute_hedges_correction_factor(len_c, len_t)\n", + " return correction_factor * d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fafb111", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def cliffs_delta(control:list|tuple|np.ndarray, \n", + " test:list|tuple|np.ndarray\n", + " )->float:\n", + " \"\"\"\n", + " Computes Cliff's delta for 2 samples.\n", + " See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)\n", + " \"\"\"\n", + " import numpy as np\n", + " from scipy.stats import mannwhitneyu\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " if control.__class__ != np.ndarray:\n", + " control = np.array(control)\n", + " if test.__class__ != np.ndarray:\n", + " test = np.array(test)\n", + "\n", + " c = control[~np.isnan(control)]\n", + " t = test[~np.isnan(test)]\n", + "\n", + " control_n = len(c)\n", + " test_n = len(t)\n", + "\n", + " # Note the order of the control and test arrays.\n", + " U, _ = mannwhitneyu(t, c, alternative='two-sided')\n", + " cliffs_delta = ((2 * U) / (control_n * test_n)) - 1\n", + "\n", + " # more = 0\n", + " # less = 0\n", + " #\n", + " # for i, c in enumerate(control):\n", + " # for j, t in enumerate(test):\n", + " # if t > c:\n", + " # more += 1\n", + " # elif t < c:\n", + " # less += 1\n", + " #\n", + " # cliffs_delta = (more - less) / (control_n * test_n)\n", + "\n", + " return cliffs_delta\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a772510", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def _compute_standardizers(control, test):\n", + " from numpy import mean, var, sqrt, nan\n", + " # For calculation of correlation; not currently used.\n", + " # from scipy.stats import pearsonr\n", + "\n", + " control_n = len(control)\n", + " test_n = len(test)\n", + "\n", + " control_mean = mean(control)\n", + " test_mean = mean(test)\n", + "\n", + " control_var = var(control, ddof=1) # use N-1 to compute the variance.\n", + " test_var = var(test, ddof=1)\n", + "\n", + " control_std = sqrt(control_var)\n", + " test_std = sqrt(test_var)\n", + "\n", + " # For unpaired 2-groups standardized mean difference.\n", + " pooled = sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) /\n", + " (control_n + test_n - 2)\n", + " )\n", + "\n", + " # For paired standardized mean difference.\n", + " average = sqrt((control_var + test_var) / 2)\n", + "\n", + " # if len(control) == len(test):\n", + " # corr = pearsonr(control, test)[0]\n", + " # std_diff = sqrt(control_var + test_var - (2 * corr * control_std * test_std))\n", + " # std_diff_corrected = std_diff / (sqrt(2 * (1 - corr)))\n", + " # return pooled, average, std_diff_corrected\n", + " #\n", + " # else:\n", + " return pooled, average # indent if you implement above code chunk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4529e82c", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def _compute_hedges_correction_factor(n1, \n", + " n2\n", + " )->float:\n", + " \"\"\"\n", + " Computes the bias correction factor for Hedges' g.\n", + "\n", + " See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g)\n", + "\n", + " `References`:\n", + " \n", + " - Larry V. Hedges & Ingram Olkin (1985).\n", + " Statistical Methods for Meta-Analysis. Orlando: Academic Press.\n", + " ISBN 0-12-336380-2.\n", + " \"\"\"\n", + "\n", + " from scipy.special import gamma\n", + " from numpy import sqrt, isinf\n", + " import warnings\n", + "\n", + " df = n1 + n2 - 2\n", + " numer = gamma(df / 2)\n", + " denom0 = gamma((df - 1) / 2)\n", + " denom = sqrt(df / 2) * denom0\n", + "\n", + " if isinf(numer) or isinf(denom):\n", + " # occurs when df is too large.\n", + " # Apply Hedges and Olkin's approximation.\n", + " df_sum = n1 + n2\n", + " denom = (4 * df_sum) - 9\n", + " out = 1 - (3 / denom)\n", + "\n", + " else:\n", + " out = numer / denom\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "249e5375", + "metadata": {}, + "outputs": [], + "source": [ + "#|export\n", + "def weighted_delta(difference, group_var):\n", + " '''\n", + " Compute the weighted deltas where the weight is the inverse of the\n", + " pooled group difference.\n", + " '''\n", + " import numpy as np\n", + "\n", + " weight = np.true_divide(1, group_var)\n", + " return np.sum(difference*weight)/np.sum(weight)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37ca3f32", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/index.qmd b/nbs/API/index.qmd new file mode 100644 index 00000000..ab16229a --- /dev/null +++ b/nbs/API/index.qmd @@ -0,0 +1,11 @@ +--- +order: 9 +title: API +listing: + fields: [title, description] + type: table + sort-ui: false + filter-ui: false +--- + +This section contains API details for each of dabest's python submodules. This reference documentation is mainly useful for people looking to customise or build on top of dabest, or wanting detailed information about how dabest works. diff --git a/nbs/API/load.ipynb b/nbs/API/load.ipynb new file mode 100644 index 00000000..24f65512 --- /dev/null +++ b/nbs/API/load.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Data\n", + "\n", + "> Loading data and relevant groups\n", + "\n", + "- order: 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _api" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def load(data, idx=None, x=None, y=None, paired=None, id_col=None,\n", + " ci=95, resamples=5000, random_seed=12345, proportional=False, \n", + " delta2 = False, experiment = None, experiment_label = None,\n", + " x1_level = None, mini_meta=False):\n", + " '''\n", + " Loads data in preparation for estimation statistics.\n", + "\n", + " This is designed to work with pandas DataFrames.\n", + "\n", + " Parameters\n", + " ----------\n", + " data : pandas DataFrame\n", + " idx : tuple\n", + " List of column names (if 'x' is not supplied) or of category names\n", + " (if 'x' is supplied). This can be expressed as a tuple of tuples,\n", + " with each individual tuple producing its own contrast plot\n", + " x : string or list, default None\n", + " Column name(s) of the independent variable. This can be expressed as\n", + " a list of 2 elements if and only if 'delta2' is True; otherwise it \n", + " can only be a string.\n", + " y : string, default None\n", + " Column names for data to be plotted on the x-axis and y-axis.\n", + " paired : string, default None\n", + " The type of the experiment under which the data are obtained. If 'paired' \n", + " is None then the data will not be treated as paired data in the subsequent\n", + " calculations. If 'paired' is 'baseline', then in each tuple of x, other \n", + " groups will be paired up with the first group (as control). If 'paired' is \n", + " 'sequential', then in each tuple of x, each group will be paired up with\n", + " its previous group (as control).\n", + " id_col : default None.\n", + " Required if `paired` is True.\n", + " ci : integer, default 95\n", + " The confidence interval width. The default of 95 produces 95%\n", + " confidence intervals.\n", + " resamples : integer, default 5000.\n", + " The number of resamples taken to generate the bootstraps which are used\n", + " to generate the confidence intervals.\n", + " random_seed : int, default 12345\n", + " This integer is used to seed the random number generator during\n", + " bootstrap resampling, ensuring that the confidence intervals\n", + " reported are replicable.\n", + " proportional : boolean, default False. \n", + " An indicator of whether the data is binary or not. When set to True, it\n", + " specifies that the data consists of binary data, where the values are\n", + " limited to 0 and 1. The code is not suitable for analyzing proportion\n", + " data that contains non-numeric values, such as strings like 'yes' and 'no'.\n", + " When False or not provided, the algorithm assumes that\n", + " the data is continuous and uses a non-proportional representation.\n", + " delta2 : boolean, default False\n", + " Indicator of delta-delta experiment\n", + " experiment : String, default None\n", + " The name of the column of the dataframe which contains the label of \n", + " experiments\n", + " experiment_lab : list, default None\n", + " A list of String to specify the order of subplots for delta-delta plots.\n", + " This can be expressed as a list of 2 elements if and only if 'delta2' \n", + " is True; otherwise it can only be a string. \n", + " x1_level : list, default None\n", + " A list of String to specify the order of subplots for delta-delta plots.\n", + " This can be expressed as a list of 2 elements if and only if 'delta2' \n", + " is True; otherwise it can only be a string. \n", + " mini_meta : boolean, default False\n", + " Indicator of weighted delta calculation.\n", + "\n", + " Returns\n", + " -------\n", + " A `Dabest` object.\n", + " '''\n", + " from ._classes import Dabest\n", + "\n", + " return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label, x1_level, mini_meta)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import dabest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create dummy data for demonstration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(88888)\n", + "N = 10\n", + "c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N)\n", + "t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N)\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1': t1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.2.14\n", + "=================\n", + " \n", + "Good evening!\n", + "The current time is Thu Mar 30 12:22:55 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n", + "my_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For proportion plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(88888)\n", + "N = 10\n", + "c1 = np.random.binomial(1, 0.2, size=N)\n", + "t1 = np.random.binomial(1, 0.5, size=N)\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1': t1})\n", + "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),proportional=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/API/misc_tools.ipynb b/nbs/API/misc_tools.ipynb new file mode 100644 index 00000000..da49407b --- /dev/null +++ b/nbs/API/misc_tools.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f570f144", + "metadata": {}, + "source": [ + "# misc_tools\n", + "\n", + "> Convenience functions that don't directly deal with plotting or bootstrap computations are placed here.\n", + "\n", + "- order: 9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ddd606b", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp misc_tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f82b1fe9", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "094b4e53", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b50da46", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def merge_two_dicts(x:dict,\n", + " y:dict\n", + " )->dict:#A dictionary containing a union of all keys in both original dicts.\n", + " \"\"\"\n", + " Given two dicts, merge them into a new dict as a shallow copy.\n", + " Any overlapping keys in `y` will override the values in `x`.\n", + "\n", + " Taken from [here](https://stackoverflow.com/questions/38987/\n", + " how-to-merge-two-python-dictionaries-in-a-single-expression)\n", + "\n", + " \"\"\"\n", + " z = x.copy()\n", + " z.update(y)\n", + " return z\n", + "\n", + "\n", + "\n", + "def unpack_and_add(l, c):\n", + " \"\"\"Convenience function to allow me to add to an existing list\n", + " without altering that list.\"\"\"\n", + " t = [a for a in l]\n", + " t.append(c)\n", + " return(t)\n", + "\n", + "\n", + "\n", + "def print_greeting():\n", + " from .__init__ import __version__\n", + " import datetime as dt\n", + " import numpy as np\n", + "\n", + " line1 = \"DABEST v{}\".format(__version__)\n", + " header = \"\".join(np.repeat(\"=\", len(line1)))\n", + " spacer = \"\".join(np.repeat(\" \", len(line1)))\n", + "\n", + " now = dt.datetime.now()\n", + " if 0 < now.hour < 12:\n", + " greeting = \"Good morning!\"\n", + " elif 12 < now.hour < 18:\n", + " greeting = \"Good afternoon!\"\n", + " else:\n", + " greeting = \"Good evening!\"\n", + "\n", + " current_time = \"The current time is {}.\".format(now.ctime())\n", + "\n", + " return \"\\n\".join([line1, header, spacer, greeting, current_time])\n", + "\n", + "\n", + "def get_varname(obj):\n", + " matching_vars = [k for k,v in globals().items() if v is obj]\n", + " if len(matching_vars) > 0:\n", + " return matching_vars[0]\n", + " else:\n", + " return \"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f6841f9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/plot_tools.ipynb b/nbs/API/plot_tools.ipynb new file mode 100644 index 00000000..675d1cef --- /dev/null +++ b/nbs/API/plot_tools.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d5d1b29", + "metadata": {}, + "source": [ + "# plot_tools\n", + "\n", + "> A set of convenience functions used for producing plots in `dabest`.\n", + "\n", + "- order: 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3eaf534c", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp plot_tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd831e92", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27f1b07c", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b070950d", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import pandas as pd\n", + "from collections import defaultdict\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import itertools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98550688", + "metadata": {}, + "outputs": [], + "source": [ + "#| export \n", + "def halfviolin(v, half='right', fill_color='k', alpha=1,\n", + " line_color='k', line_width=0):\n", + " import numpy as np\n", + "\n", + " for b in v['bodies']:\n", + " V = b.get_paths()[0].vertices\n", + "\n", + " mean_vertical = np.mean(V[:, 0])\n", + " mean_horizontal = np.mean(V[:, 1])\n", + "\n", + " if half == 'right':\n", + " V[:, 0] = np.clip(V[:, 0], mean_vertical, np.inf)\n", + " elif half == 'left':\n", + " V[:, 0] = np.clip(V[:, 0], -np.inf, mean_vertical)\n", + " elif half == 'bottom':\n", + " V[:, 1] = np.clip(V[:, 1], -np.inf, mean_horizontal)\n", + " elif half == 'top':\n", + " V[:, 1] = np.clip(V[:, 1], mean_horizontal, np.inf)\n", + "\n", + " b.set_color(fill_color)\n", + " b.set_alpha(alpha)\n", + " b.set_edgecolor(line_color)\n", + " b.set_linewidth(line_width)\n", + "\n", + "\n", + "def get_swarm_spans(coll):\n", + " \"\"\"\n", + " Given a matplotlib Collection, will obtain the x and y spans\n", + " for the collection. Will return None if this fails.\n", + " \"\"\"\n", + " import numpy as np\n", + " x, y = np.array(coll.get_offsets()).T\n", + " try:\n", + " return x.min(), x.max(), y.min(), y.max()\n", + " except ValueError:\n", + " return None\n", + "\n", + "def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format.\n", + " x:str, #x column to be plotted.\n", + " y:str, # y column to be plotted.\n", + " type:str='mean_sd', # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead.\n", + " offset:float=0.2, #Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets.\n", + " ax=None, #If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used.\n", + " line_color=\"black\", gap_width_percent=1, \n", + " pos:list=[0, 1],#The positions of the error bars for the sankey_error_bar method.\n", + " method:str='gapped_lines', #The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'.\n", + " **kwargs:dict\n", + " ):\n", + " '''\n", + " Function to plot the standard deviations as vertical errorbars.\n", + " The mean is a gap defined by negative space.\n", + "\n", + " This function combines the functionality of gapped_lines(),\n", + " proportional_error_bar(), and sankey_error_bar().\n", + "\n", + " '''\n", + " import numpy as np\n", + " import pandas as pd\n", + " import matplotlib.pyplot as plt\n", + " import matplotlib.lines as mlines\n", + "\n", + " if gap_width_percent < 0 or gap_width_percent > 100:\n", + " raise ValueError(\"`gap_width_percent` must be between 0 and 100.\")\n", + " if method not in ['gapped_lines', 'proportional_error_bar', 'sankey_error_bar']:\n", + " raise ValueError(\"Invalid `method`. Must be one of 'gapped_lines', 'proportional_error_bar', or 'sankey_error_bar'.\")\n", + "\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " ax_ylims = ax.get_ylim()\n", + " ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0])\n", + " gap_width = ax_yspan * gap_width_percent / 100\n", + "\n", + " keys = kwargs.keys()\n", + " if 'clip_on' not in keys:\n", + " kwargs['clip_on'] = False\n", + "\n", + " if 'zorder' not in keys:\n", + " kwargs['zorder'] = 5\n", + "\n", + " if 'lw' not in keys:\n", + " kwargs['lw'] = 2.\n", + "\n", + " if isinstance(data[x].dtype, pd.CategoricalDtype):\n", + " group_order = pd.unique(data[x]).categories\n", + " else:\n", + " group_order = pd.unique(data[x])\n", + "\n", + " means = data.groupby(x)[y].mean().reindex(index=group_order)\n", + "\n", + " if method in ['proportional_error_bar', 'sankey_error_bar']:\n", + " g = lambda x: np.sqrt((np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x)))\n", + " sd = data.groupby(x)[y].apply(g)\n", + " else:\n", + " sd = data.groupby(x)[y].std().reindex(index=group_order)\n", + "\n", + " lower_sd = means - sd\n", + " upper_sd = means + sd\n", + "\n", + " if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():\n", + " kwargs['clip_on'] = True\n", + "\n", + " medians = data.groupby(x)[y].median().reindex(index=group_order)\n", + " quantiles = data.groupby(x)[y].quantile([0.25, 0.75]) \\\n", + " .unstack() \\\n", + " .reindex(index=group_order)\n", + " lower_quartiles = quantiles[0.25]\n", + " upper_quartiles = quantiles[0.75]\n", + "\n", + " if type == 'mean_sd':\n", + " central_measures = means\n", + " lows = lower_sd\n", + " highs = upper_sd\n", + " elif type == 'median_quartiles':\n", + " central_measures = medians\n", + " lows = lower_quartiles\n", + " highs = upper_quartiles\n", + "\n", + " n_groups = len(central_measures)\n", + "\n", + " if isinstance(line_color, str):\n", + " custom_palette = np.repeat(line_color, n_groups)\n", + " else:\n", + " if len(line_color) != n_groups:\n", + " err1 = \"{} groups are being plotted, but \".format(n_groups)\n", + " err2 = \"{} colors(s) were supplied in `line_color`.\".format(len(line_color))\n", + " raise ValueError(err1 + err2)\n", + " custom_palette = line_color\n", + "\n", + " try:\n", + " len_offset = len(offset)\n", + " except TypeError:\n", + " offset = np.repeat(offset, n_groups)\n", + " len_offset = len(offset)\n", + "\n", + " if len_offset != n_groups:\n", + " err1 = \"{} groups are being plotted, but \".format(n_groups)\n", + " err2 = \"{} offset(s) were supplied in `offset`.\".format(len_offset)\n", + " raise ValueError(err1 + err2)\n", + "\n", + " kwargs['zorder'] = kwargs['zorder']\n", + "\n", + " for xpos, central_measure in enumerate(central_measures):\n", + " kwargs['color'] = custom_palette[xpos]\n", + "\n", + " if method == 'sankey_error_bar':\n", + " _xpos = pos[xpos] + offset[xpos]\n", + " else:\n", + " _xpos = xpos + offset[xpos]\n", + "\n", + " low = lows[xpos]\n", + " low_to_mean = mlines.Line2D([_xpos, _xpos],\n", + " [low, central_measure - gap_width],\n", + " **kwargs)\n", + " ax.add_line(low_to_mean)\n", + "\n", + " high = highs[xpos]\n", + " mean_to_high = mlines.Line2D([_xpos, _xpos],\n", + " [central_measure + gap_width, high],\n", + " **kwargs)\n", + " ax.add_line(mean_to_high)\n", + "\n", + "def check_data_matches_labels(labels,#list of input labels \n", + " data, #Pandas Series of input data\n", + " side:str # 'left' or 'right' on the sankey diagram\n", + " ):\n", + " '''\n", + " Function to check that the labels and data match in the sankey diagram. \n", + " And enforce labels and data to be lists.\n", + " Raises an exception if the labels and data do not match.\n", + " '''\n", + " if len(labels > 0):\n", + " if isinstance(data, list):\n", + " data = set(data)\n", + " if isinstance(data, pd.Series):\n", + " data = set(data.unique())\n", + " if isinstance(labels, list):\n", + " labels = set(labels)\n", + " if labels != data:\n", + " msg = \"\\n\"\n", + " if len(labels) <= 20:\n", + " msg = \"Labels: \" + \",\".join(labels) + \"\\n\"\n", + " if len(data) < 20:\n", + " msg += \"Data: \" + \",\".join(data)\n", + " raise Exception('{0} labels and data do not match.{1}'.format(side, msg))\n", + " \n", + "def normalize_dict(nested_dict, target):\n", + " val = {}\n", + " for key in nested_dict.keys():\n", + " val[key] = np.sum([nested_dict[sub_key][key] for sub_key in nested_dict.keys()])\n", + " \n", + " for key, value in nested_dict.items():\n", + " if isinstance(value, dict):\n", + " for subkey in value.keys():\n", + " value[subkey] = value[subkey] * target[subkey]['right']/val[subkey]\n", + " return nested_dict\n", + "\n", + "def single_sankey(left:np.array,# data on the left of the diagram\n", + " right:np.array, # data on the right of the diagram, len(left) == len(right)\n", + " xpos:float=0, # the starting point on the x-axis\n", + " leftWeight:np.array=None, #weights for the left labels, if None, all weights are 1\n", + " rightWeight:np.array=None, #weights for the right labels, if None, all weights are corresponding leftWeight\n", + " colorDict:dict=None, #input format: {'label': 'color'}\n", + " leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels.\n", + " rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels.\n", + " ax=None, #matplotlib axes to be drawn on\n", + " width=0.5, \n", + " alpha=0.65, \n", + " bar_width=0.2, \n", + " rightColor:bool=False, #if True, each strip of the diagram will be colored according to the corresponding left labels\n", + " align:bool='center'# if 'center', the diagram will be centered on each xtick, if 'edge', the diagram will be aligned with the left edge of each xtick\n", + " ):\n", + "\n", + " '''\n", + " Make a single Sankey diagram showing proportion flow from left to right\n", + " Original code from: https://github.com/anazalea/pySankey\n", + " Changes are added to normalize each diagram's height to be 1\n", + "\n", + " '''\n", + "\n", + " # Initiating values\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " if leftWeight is None:\n", + " leftWeight = []\n", + " if rightWeight is None:\n", + " rightWeight = []\n", + " if leftLabels is None:\n", + " leftLabels = []\n", + " if rightLabels is None:\n", + " rightLabels = []\n", + " # Check weights\n", + " if len(leftWeight) == 0:\n", + " leftWeight = np.ones(len(left))\n", + " if len(rightWeight) == 0:\n", + " rightWeight = leftWeight\n", + "\n", + " # Create Dataframe\n", + " if isinstance(left, pd.Series):\n", + " left.reset_index(drop=True, inplace=True)\n", + " if isinstance(right, pd.Series):\n", + " right.reset_index(drop=True, inplace=True)\n", + " dataFrame = pd.DataFrame({'left': left, 'right': right, 'leftWeight': leftWeight,\n", + " 'rightWeight': rightWeight}, index=range(len(left)))\n", + " \n", + " if dataFrame[['left', 'right']].isnull().any(axis=None):\n", + " raise Exception('Sankey graph does not support null values.')\n", + "\n", + " # Identify all labels that appear 'left' or 'right'\n", + " allLabels = pd.Series(np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1]).unique()\n", + "\n", + " # Identify left labels\n", + " if len(leftLabels) == 0:\n", + " leftLabels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique()\n", + " else:\n", + " check_data_matches_labels(leftLabels, dataFrame['left'], 'left')\n", + "\n", + " # Identify right labels\n", + " if len(rightLabels) == 0:\n", + " rightLabels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique()\n", + " else:\n", + " check_data_matches_labels(leftLabels, dataFrame['right'], 'right')\n", + "\n", + " # If no colorDict given, make one\n", + " if colorDict is None:\n", + " colorDict = {}\n", + " palette = \"hls\"\n", + " colorPalette = sns.color_palette(palette, len(allLabels))\n", + " for i, label in enumerate(allLabels):\n", + " colorDict[label] = colorPalette[i]\n", + " fail_color = {0:\"grey\"}\n", + " colorDict.update(fail_color)\n", + " else:\n", + " missing = [label for label in allLabels if label not in colorDict.keys()]\n", + " if missing:\n", + " msg = \"The palette parameter is missing values for the following labels : \"\n", + " msg += '{}'.format(', '.join(missing))\n", + " raise ValueError(msg)\n", + "\n", + " if align not in (\"center\", \"edge\"):\n", + " err = '{} assigned for `align` is not valid.'.format(align)\n", + " raise ValueError(err)\n", + " if align == \"center\":\n", + " try:\n", + " leftpos = xpos - width / 2\n", + " except TypeError as e:\n", + " raise TypeError(f'the dtypes of parameters x ({xpos.dtype}) '\n", + " f'and width ({width.dtype}) '\n", + " f'are incompatible') from e\n", + " else: \n", + " leftpos = xpos\n", + "\n", + " # Combine left and right arrays to have a pandas.DataFrame in the 'long' format\n", + " left_series = pd.Series(left, name='values').to_frame().assign(groups='left')\n", + " right_series = pd.Series(right, name='values').to_frame().assign(groups='right')\n", + " concatenated_df = pd.concat([left_series, right_series], ignore_index=True)\n", + "\n", + " # Determine positions of left label patches and total widths\n", + " # We also want the height of the graph to be 1\n", + " leftWidths_norm = defaultdict()\n", + " for i, leftLabel in enumerate(leftLabels):\n", + " myD = {}\n", + " myD['left'] = (dataFrame[dataFrame.left == leftLabel].leftWeight.sum()/ \\\n", + " dataFrame.leftWeight.sum())*(1-(len(leftLabels)-1)*0.02)\n", + " if i == 0:\n", + " myD['bottom'] = 0\n", + " myD['top'] = myD['left']\n", + " else:\n", + " myD['bottom'] = leftWidths_norm[leftLabels[i - 1]]['top'] + 0.02\n", + " myD['top'] = myD['bottom'] + myD['left']\n", + " topEdge = myD['top']\n", + " leftWidths_norm[leftLabel] = myD\n", + "\n", + " # Determine positions of right label patches and total widths\n", + " rightWidths_norm = defaultdict()\n", + " for i, rightLabel in enumerate(rightLabels):\n", + " myD = {}\n", + " myD['right'] = (dataFrame[dataFrame.right == rightLabel].rightWeight.sum()/ \\\n", + " dataFrame.rightWeight.sum())*(1-(len(leftLabels)-1)*0.02)\n", + " if i == 0:\n", + " myD['bottom'] = 0\n", + " myD['top'] = myD['right']\n", + " else:\n", + " myD['bottom'] = rightWidths_norm[rightLabels[i - 1]]['top'] + 0.02\n", + " myD['top'] = myD['bottom'] + myD['right']\n", + " topEdge = myD['top']\n", + " rightWidths_norm[rightLabel] = myD \n", + "\n", + " # Total width of the graph\n", + " xMax = width\n", + "\n", + " # Determine widths of individual strips, all widths are normalized to 1\n", + " ns_l = defaultdict()\n", + " ns_r = defaultdict()\n", + " ns_l_norm = defaultdict()\n", + " ns_r_norm = defaultdict()\n", + " for leftLabel in leftLabels:\n", + " leftDict = {}\n", + " rightDict = {}\n", + " for rightLabel in rightLabels:\n", + " leftDict[rightLabel] = dataFrame[\n", + " (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)\n", + " ].leftWeight.sum()\n", + " \n", + " rightDict[rightLabel] = dataFrame[\n", + " (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)\n", + " ].rightWeight.sum()\n", + " factorleft = leftWidths_norm[leftLabel]['left']/sum(leftDict.values())\n", + " leftDict_norm = {k: v*factorleft for k, v in leftDict.items()}\n", + " ns_l_norm[leftLabel] = leftDict_norm\n", + " ns_r[leftLabel] = rightDict\n", + " \n", + " # ns_r should be using a different way of normalization to fit the right side\n", + " # It is normalized using the value with the same key in each sub-dictionary\n", + "\n", + " ns_r_norm = normalize_dict(ns_r, rightWidths_norm)\n", + "\n", + " # Plot vertical bars for each label\n", + " for leftLabel in leftLabels:\n", + " ax.fill_between(\n", + " [leftpos + (-(bar_width) * xMax), leftpos],\n", + " 2 * [leftWidths_norm[leftLabel][\"bottom\"]],\n", + " 2 * [leftWidths_norm[leftLabel][\"bottom\"] + leftWidths_norm[leftLabel][\"left\"]],\n", + " color=colorDict[leftLabel],\n", + " alpha=0.99,\n", + " )\n", + " for rightLabel in rightLabels:\n", + " ax.fill_between(\n", + " [xMax + leftpos, leftpos + ((1 + bar_width) * xMax)], \n", + " 2 * [rightWidths_norm[rightLabel]['bottom']],\n", + " 2 * [rightWidths_norm[rightLabel]['bottom'] + rightWidths_norm[rightLabel]['right']],\n", + " color=colorDict[rightLabel],\n", + " alpha=0.99\n", + " )\n", + "\n", + " # Plot error bars\n", + " error_bar(concatenated_df, x='groups', y='values', ax=ax, offset=0, gap_width_percent=2,\n", + " method=\"sankey_error_bar\",\n", + " pos=[(leftpos + (-(bar_width) * xMax) + leftpos)/2, \\\n", + " (xMax + leftpos + leftpos + ((1 + bar_width) * xMax))/2])\n", + " \n", + " # Plot strips\n", + " for leftLabel, rightLabel in itertools.product(leftLabels, rightLabels):\n", + " labelColor = leftLabel\n", + " if rightColor:\n", + " labelColor = rightLabel\n", + " if len(dataFrame[(dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)]) > 0:\n", + " # Create array of y values for each strip, half at left value,\n", + " # half at right, convolve\n", + " ys_d = np.array(50 * [leftWidths_norm[leftLabel]['bottom']] + \\\n", + " 50 * [rightWidths_norm[rightLabel]['bottom']])\n", + " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid')\n", + " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid')\n", + " ys_u = np.array(50 * [leftWidths_norm[leftLabel]['bottom'] + ns_l_norm[leftLabel][rightLabel]] + \\\n", + " 50 * [rightWidths_norm[rightLabel]['bottom'] + ns_r_norm[leftLabel][rightLabel]])\n", + " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid')\n", + " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid')\n", + "\n", + " # Update bottom edges at each label so next strip starts at the right place\n", + " leftWidths_norm[leftLabel]['bottom'] += ns_l_norm[leftLabel][rightLabel]\n", + " rightWidths_norm[rightLabel]['bottom'] += ns_r_norm[leftLabel][rightLabel]\n", + " ax.fill_between(\n", + " np.linspace(leftpos, leftpos + xMax, len(ys_d)), ys_d, ys_u, alpha=alpha,\n", + " color=colorDict[labelColor], edgecolor='none'\n", + " )\n", + " \n", + "def sankeydiag(data:pd.DataFrame,\n", + " xvar:str, # x column to be plotted.\n", + " yvar:str, # y column to be plotted.\n", + " left_idx:str, #the value in column xvar that is on the left side of each sankey diagram\n", + " right_idx:str, #the value in column xvar that is on the right side of each sankey diagram, if len(left_idx) == 1, it will be broadcasted to the same length as right_idx, otherwise it should have the same length as right_idx\n", + " leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels.\n", + " rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels.\n", + " palette:str|dict=None, \n", + " ax=None, #matplotlib axes to be drawn on\n", + " one_sankey:bool=False,# determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes\n", + " width:float=0.4, # the width of each sankey diagram\n", + " rightColor:bool=False,#if True, each strip of the diagram will be colored according to the corresponding left labels\n", + " align:str='center', #the alignment of each sankey diagram, can be 'center' or 'left'\n", + " alpha:float=0.65, #the transparency of each strip\n", + " **kwargs):\n", + " '''\n", + " Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes\n", + " using the value in column yvar according to the value in column xvar\n", + " left_idx in the column xvar is on the left side of each sankey diagram\n", + " right_idx in the column xvar is on the right side of each sankey diagram\n", + "\n", + " '''\n", + "\n", + " import numpy as np\n", + " import pandas as pd\n", + " import seaborn as sns\n", + " import matplotlib.pyplot as plt\n", + "\n", + " if \"width\" in kwargs:\n", + " width = kwargs[\"width\"]\n", + "\n", + " if \"align\" in kwargs:\n", + " align = kwargs[\"align\"]\n", + " \n", + " if \"alpha\" in kwargs:\n", + " alpha = kwargs[\"alpha\"]\n", + " \n", + " if \"rightColor\" in kwargs:\n", + " rightColor = kwargs[\"rightColor\"]\n", + " \n", + " if \"bar_width\" in kwargs:\n", + " bar_width = kwargs[\"bar_width\"]\n", + "\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique()\n", + " \n", + " # Check if all the elements in left_idx and right_idx are in xvar column\n", + " unique_xvar = data[xvar].unique()\n", + " if not all(elem in unique_xvar for elem in left_idx):\n", + " raise ValueError(f\"{left_idx} not found in {xvar} column\")\n", + " if not all(elem in unique_xvar for elem in right_idx):\n", + " raise ValueError(f\"{right_idx} not found in {xvar} column\")\n", + "\n", + " xpos = 0\n", + "\n", + " # For baseline comparison, broadcast left_idx to the same length as right_idx\n", + " # so that the left of sankey diagram will be the same\n", + " # For sequential comparison, left_idx and right_idx can have anything different \n", + " # but should have the same length\n", + " if len(left_idx) == 1:\n", + " broadcasted_left = np.broadcast_to(left_idx, len(right_idx))\n", + " elif len(left_idx) != len(right_idx):\n", + " raise ValueError(f\"left_idx and right_idx should have the same length\")\n", + " else:\n", + " broadcasted_left = left_idx\n", + "\n", + " if isinstance(palette, dict):\n", + " if not all(key in allLabels for key in palette.keys()):\n", + " raise ValueError(f\"keys in palette should be in {yvar} column\")\n", + " else: \n", + " plot_palette = palette\n", + " elif isinstance(palette, str):\n", + " plot_palette = {}\n", + " colorPalette = sns.color_palette(palette, len(allLabels))\n", + " for i, label in enumerate(allLabels):\n", + " plot_palette[label] = colorPalette[i]\n", + " else:\n", + " plot_palette = None\n", + "\n", + " for left, right in zip(broadcasted_left, right_idx):\n", + " if one_sankey == False:\n", + " single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], \n", + " xpos=xpos, ax=ax, colorDict=plot_palette, width=width, \n", + " leftLabels=leftLabels, rightLabels=rightLabels, \n", + " rightColor=rightColor, bar_width=bar_width,\n", + " align=align, alpha=alpha)\n", + " xpos += 1\n", + " else:\n", + " xpos = 0 + bar_width/2\n", + " width = 1 - bar_width\n", + " single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], \n", + " xpos=xpos, ax=ax, colorDict=plot_palette, width=width, \n", + " leftLabels=leftLabels, rightLabels=rightLabels, \n", + " rightColor=rightColor, bar_width=bar_width,\n", + " align='edge', alpha=alpha)\n", + "\n", + " if one_sankey == False:\n", + " sankey_ticks = [f\"{left}\\n v.s.\\n{right}\" for left, right in zip(broadcasted_left, right_idx)]\n", + " ax.get_xaxis().set_ticks(np.arange(len(right_idx)))\n", + " ax.get_xaxis().set_ticklabels(sankey_ticks)\n", + " else:\n", + " sankey_ticks = [broadcasted_left[0], right_idx[0]]\n", + " ax.set_xticks([0, 1])\n", + " ax.set_xticklabels(sankey_ticks)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43844ece", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/plotter.ipynb b/nbs/API/plotter.ipynb new file mode 100644 index 00000000..e7100d0c --- /dev/null +++ b/nbs/API/plotter.ipynb @@ -0,0 +1,1210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "984371b3", + "metadata": {}, + "source": [ + "# Plot\n", + "\n", + "> Creating estimation plots.\n", + "\n", + "- order: 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "112c011a", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp plotter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90c48b71", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68fce47a", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36a42b1c", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):\n", + " \"\"\"\n", + " Custom function that creates an estimation plot from an EffectSizeDataFrame.\n", + " \n", + " Parameters\n", + " ----------\n", + " EffectSizeDataFrame\n", + " A `dabest` EffectSizeDataFrame object.\n", + " plot_kwargs\n", + " color_col=None\n", + " raw_marker_size=6, es_marker_size=9,\n", + " swarm_label=None, contrast_label=None, delta2_label=None,\n", + " swarm_ylim=None, contrast_ylim=None, delta2_ylim=None,\n", + " custom_palette=None, swarm_desat=0.5, halfviolin_desat=1,\n", + " halfviolin_alpha=0.8,\n", + " face_color = None,\n", + " bar_label=None, bar_desat=0.8, bar_width = 0.5,bar_ylim = None,\n", + " ci=None, ci_type='bca', err_color=None,\n", + " float_contrast=True,\n", + " show_pairs=True,\n", + " show_delta2=True,\n", + " group_summaries=None,\n", + " group_summaries_offset=0.1,\n", + " fig_size=None,\n", + " dpi=100,\n", + " ax=None,\n", + " swarmplot_kwargs=None,\n", + " violinplot_kwargs=None,\n", + " slopegraph_kwargs=None,\n", + " sankey_kwargs=None,\n", + " reflines_kwargs=None,\n", + " group_summary_kwargs=None,\n", + " legend_kwargs=None,\n", + " \"\"\"\n", + "\n", + " import numpy as np\n", + " import seaborn as sns\n", + " import matplotlib.pyplot as plt\n", + " import pandas as pd\n", + " import warnings\n", + " warnings.filterwarnings('ignore', 'This figure includes Axes that are not compatible with tight_layout')\n", + "\n", + " from .misc_tools import merge_two_dicts\n", + " from .plot_tools import halfviolin, get_swarm_spans, error_bar, sankeydiag\n", + " from ._stats_tools.effsize import _compute_standardizers, _compute_hedges_correction_factor\n", + "\n", + " import logging\n", + " # Have to disable logging of warning when get_legend_handles_labels()\n", + " # tries to get from slopegraph.\n", + " logging.disable(logging.WARNING)\n", + "\n", + " # Save rcParams that I will alter, so I can reset back.\n", + " original_rcParams = {}\n", + " _changed_rcParams = ['axes.grid']\n", + " for parameter in _changed_rcParams:\n", + " original_rcParams[parameter] = plt.rcParams[parameter]\n", + "\n", + " plt.rcParams['axes.grid'] = False\n", + "\n", + " ytick_color = plt.rcParams[\"ytick.color\"]\n", + " face_color = plot_kwargs[\"face_color\"]\n", + " if plot_kwargs[\"face_color\"] is None:\n", + " face_color = \"white\"\n", + "\n", + " dabest_obj = EffectSizeDataFrame.dabest_obj\n", + " plot_data = EffectSizeDataFrame._plot_data\n", + " xvar = EffectSizeDataFrame.xvar\n", + " yvar = EffectSizeDataFrame.yvar\n", + " is_paired = EffectSizeDataFrame.is_paired\n", + " delta2 = EffectSizeDataFrame.delta2\n", + " mini_meta = EffectSizeDataFrame.mini_meta\n", + " effect_size = EffectSizeDataFrame.effect_size\n", + " proportional = EffectSizeDataFrame.proportional\n", + "\n", + " all_plot_groups = dabest_obj._all_plot_groups\n", + " idx = dabest_obj.idx\n", + "\n", + " if effect_size != \"mean_diff\" or not delta2:\n", + " show_delta2 = False\n", + " else:\n", + " show_delta2 = plot_kwargs[\"show_delta2\"]\n", + "\n", + " if effect_size != \"mean_diff\" or not mini_meta:\n", + " show_mini_meta = False\n", + " else:\n", + " show_mini_meta = plot_kwargs[\"show_mini_meta\"]\n", + "\n", + " if show_delta2 and show_mini_meta:\n", + " err0 = \"`show_delta2` and `show_mini_meta` cannot be True at the same time.\"\n", + " raise ValueError(err0)\n", + "\n", + " # Disable Gardner-Altman plotting if any of the idxs comprise of more than\n", + " # two groups or if it is a delta-delta plot.\n", + " float_contrast = plot_kwargs[\"float_contrast\"]\n", + " effect_size_type = EffectSizeDataFrame.effect_size\n", + " if len(idx) > 1 or len(idx[0]) > 2:\n", + " float_contrast = False\n", + "\n", + " if effect_size_type in ['cliffs_delta']:\n", + " float_contrast = False\n", + "\n", + " if show_delta2 or show_mini_meta:\n", + " float_contrast = False \n", + "\n", + " if not is_paired:\n", + " show_pairs = False\n", + " else:\n", + " show_pairs = plot_kwargs[\"show_pairs\"]\n", + "\n", + " # Set default kwargs first, then merge with user-dictated ones.\n", + " default_swarmplot_kwargs = {'size': plot_kwargs[\"raw_marker_size\"]}\n", + " if plot_kwargs[\"swarmplot_kwargs\"] is None:\n", + " swarmplot_kwargs = default_swarmplot_kwargs\n", + " else:\n", + " swarmplot_kwargs = merge_two_dicts(default_swarmplot_kwargs,\n", + " plot_kwargs[\"swarmplot_kwargs\"])\n", + "\n", + " # Barplot kwargs\n", + " default_barplot_kwargs = {\"estimator\": np.mean, \"ci\": plot_kwargs[\"ci\"]}\n", + "\n", + " if plot_kwargs[\"barplot_kwargs\"] is None:\n", + " barplot_kwargs = default_barplot_kwargs\n", + " else:\n", + " barplot_kwargs = merge_two_dicts(default_barplot_kwargs,\n", + " plot_kwargs[\"barplot_kwargs\"])\n", + "\n", + " # Sankey Diagram kwargs\n", + " default_sankey_kwargs = {\"width\": 0.4, \"align\": \"center\",\n", + " \"alpha\": 0.4, \"rightColor\": False,\n", + " \"bar_width\":0.2}\n", + " if plot_kwargs[\"sankey_kwargs\"] is None:\n", + " sankey_kwargs = default_sankey_kwargs\n", + " else:\n", + " sankey_kwargs = merge_two_dicts(default_sankey_kwargs,\n", + " plot_kwargs[\"sankey_kwargs\"])\n", + " \n", + "\n", + " # Violinplot kwargs.\n", + " default_violinplot_kwargs = {'widths':0.5, 'vert':True,\n", + " 'showextrema':False, 'showmedians':False}\n", + " if plot_kwargs[\"violinplot_kwargs\"] is None:\n", + " violinplot_kwargs = default_violinplot_kwargs\n", + " else:\n", + " violinplot_kwargs = merge_two_dicts(default_violinplot_kwargs,\n", + " plot_kwargs[\"violinplot_kwargs\"])\n", + "\n", + " # slopegraph kwargs.\n", + " default_slopegraph_kwargs = {'lw':1, 'alpha':0.5}\n", + " if plot_kwargs[\"slopegraph_kwargs\"] is None:\n", + " slopegraph_kwargs = default_slopegraph_kwargs\n", + " else:\n", + " slopegraph_kwargs = merge_two_dicts(default_slopegraph_kwargs,\n", + " plot_kwargs[\"slopegraph_kwargs\"])\n", + "\n", + " # Zero reference-line kwargs.\n", + " default_reflines_kwargs = {'linestyle':'solid', 'linewidth':0.75,\n", + " 'zorder': 2,\n", + " 'color': ytick_color}\n", + " if plot_kwargs[\"reflines_kwargs\"] is None:\n", + " reflines_kwargs = default_reflines_kwargs\n", + " else:\n", + " reflines_kwargs = merge_two_dicts(default_reflines_kwargs,\n", + " plot_kwargs[\"reflines_kwargs\"])\n", + "\n", + " # Legend kwargs.\n", + " default_legend_kwargs = {'loc': 'upper left', 'frameon': False}\n", + " if plot_kwargs[\"legend_kwargs\"] is None:\n", + " legend_kwargs = default_legend_kwargs\n", + " else:\n", + " legend_kwargs = merge_two_dicts(default_legend_kwargs,\n", + " plot_kwargs[\"legend_kwargs\"])\n", + "\n", + " # Group summaries kwargs.\n", + " gs_default = {'mean_sd', 'median_quartiles', None}\n", + " if plot_kwargs[\"group_summaries\"] not in gs_default:\n", + " raise ValueError('group_summaries must be one of'\n", + " ' these: {}.'.format(gs_default) )\n", + "\n", + " default_group_summary_kwargs = {'zorder': 3, 'lw': 2,\n", + " 'alpha': 1}\n", + " if plot_kwargs[\"group_summary_kwargs\"] is None:\n", + " group_summary_kwargs = default_group_summary_kwargs\n", + " else:\n", + " group_summary_kwargs = merge_two_dicts(default_group_summary_kwargs,\n", + " plot_kwargs[\"group_summary_kwargs\"])\n", + "\n", + " # Create color palette that will be shared across subplots.\n", + " color_col = plot_kwargs[\"color_col\"]\n", + " if color_col is None:\n", + " color_groups = pd.unique(plot_data[xvar])\n", + " bootstraps_color_by_group = True\n", + " else:\n", + " if color_col not in plot_data.columns:\n", + " raise KeyError(\"``{}`` is not a column in the data.\".format(color_col))\n", + " color_groups = pd.unique(plot_data[color_col])\n", + " bootstraps_color_by_group = False\n", + " if show_pairs:\n", + " bootstraps_color_by_group = False\n", + "\n", + " # Handle the color palette.\n", + " names = color_groups\n", + " n_groups = len(color_groups)\n", + " custom_pal = plot_kwargs[\"custom_palette\"]\n", + " swarm_desat = plot_kwargs[\"swarm_desat\"]\n", + " bar_desat = plot_kwargs[\"bar_desat\"]\n", + " contrast_desat = plot_kwargs[\"halfviolin_desat\"]\n", + "\n", + " if custom_pal is None:\n", + " unsat_colors = sns.color_palette(n_colors=n_groups)\n", + " else:\n", + "\n", + " if isinstance(custom_pal, dict):\n", + " groups_in_palette = {k: v for k,v in custom_pal.items()\n", + " if k in color_groups}\n", + "\n", + " # # check that all the keys in custom_pal are found in the\n", + " # # color column.\n", + " # col_grps = {k for k in color_groups}\n", + " # pal_grps = {k for k in custom_pal.keys()}\n", + " # not_in_pal = pal_grps.difference(col_grps)\n", + " # if len(not_in_pal) > 0:\n", + " # err1 = 'The custom palette keys {} '.format(not_in_pal)\n", + " # err2 = 'are not found in `{}`. Please check.'.format(color_col)\n", + " # errstring = (err1 + err2)\n", + " # raise IndexError(errstring)\n", + "\n", + " names = groups_in_palette.keys()\n", + " unsat_colors = groups_in_palette.values()\n", + "\n", + " elif isinstance(custom_pal, list):\n", + " unsat_colors = custom_pal[0: n_groups]\n", + "\n", + " elif isinstance(custom_pal, str):\n", + " # check it is in the list of matplotlib palettes.\n", + " if custom_pal in plt.colormaps():\n", + " unsat_colors = sns.color_palette(custom_pal, n_groups)\n", + " else:\n", + " err1 = 'The specified `custom_palette` {}'.format(custom_pal)\n", + " err2 = ' is not a matplotlib palette. Please check.'\n", + " raise ValueError(err1 + err2)\n", + "\n", + " if custom_pal is None and color_col is None:\n", + " swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]\n", + " plot_palette_raw = dict(zip(names.categories, swarm_colors))\n", + "\n", + " bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]\n", + " plot_palette_bar = dict(zip(names.categories, bar_color))\n", + "\n", + " contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n", + " plot_palette_contrast = dict(zip(names.categories, contrast_colors))\n", + "\n", + " # For Sankey Diagram plot, no need to worry about the color, each bar will have the same two colors\n", + " # default color palette will be set to \"hls\"\n", + " plot_palette_sankey = None\n", + "\n", + " else:\n", + " swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]\n", + " plot_palette_raw = dict(zip(names, swarm_colors))\n", + "\n", + " bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]\n", + " plot_palette_bar = dict(zip(names, bar_color))\n", + "\n", + " contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n", + " plot_palette_contrast = dict(zip(names, contrast_colors))\n", + "\n", + " plot_palette_sankey = custom_pal\n", + "\n", + " # Infer the figsize.\n", + " fig_size = plot_kwargs[\"fig_size\"]\n", + " if fig_size is None:\n", + " all_groups_count = np.sum([len(i) for i in dabest_obj.idx])\n", + " # Increase the width for delta-delta graph\n", + " if show_delta2 or show_mini_meta:\n", + " all_groups_count += 2\n", + " if is_paired and show_pairs is True and proportional is False:\n", + " frac = 0.75\n", + " else:\n", + " frac = 1\n", + " if float_contrast is True:\n", + " height_inches = 4\n", + " each_group_width_inches = 2.5 * frac\n", + " else:\n", + " height_inches = 6\n", + " each_group_width_inches = 1.5 * frac\n", + "\n", + " width_inches = (each_group_width_inches * all_groups_count)\n", + " fig_size = (width_inches, height_inches)\n", + "\n", + " # Initialise the figure.\n", + " # sns.set(context=\"talk\", style='ticks')\n", + " init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs[\"dpi\"]\n", + " ,tight_layout=True)\n", + "\n", + " width_ratios_ga = [2.5, 1]\n", + " h_space_cummings = 0.3\n", + " if plot_kwargs[\"ax\"] is not None:\n", + " # New in v0.2.6.\n", + " # Use inset axes to create the estimation plot inside a single axes.\n", + " # Author: Adam L Nekimken. (PR #73)\n", + " inset_contrast = True\n", + " rawdata_axes = plot_kwargs[\"ax\"]\n", + " ax_position = rawdata_axes.get_position() # [[x0, y0], [x1, y1]]\n", + " \n", + " fig = rawdata_axes.get_figure()\n", + " fig.patch.set_facecolor(face_color)\n", + " \n", + " if float_contrast is True:\n", + " axins = rawdata_axes.inset_axes(\n", + " [1, 0,\n", + " width_ratios_ga[1]/width_ratios_ga[0], 1])\n", + " rawdata_axes.set_position( # [l, b, w, h]\n", + " [ax_position.x0,\n", + " ax_position.y0,\n", + " (ax_position.x1 - ax_position.x0) * (width_ratios_ga[0] /\n", + " sum(width_ratios_ga)),\n", + " (ax_position.y1 - ax_position.y0)])\n", + "\n", + " contrast_axes = axins\n", + "\n", + " else:\n", + " axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])\n", + " plot_height = ((ax_position.y1 - ax_position.y0) /\n", + " (2 + h_space_cummings))\n", + " rawdata_axes.set_position(\n", + " [ax_position.x0,\n", + " ax_position.y0 + (1 + h_space_cummings) * plot_height,\n", + " (ax_position.x1 - ax_position.x0),\n", + " plot_height])\n", + "\n", + " # If the contrast axes are NOT floating, create lists to store\n", + " # raw ylims and raw tick intervals, so that I can normalize\n", + " # their ylims later.\n", + " contrast_ax_ylim_low = list()\n", + " contrast_ax_ylim_high = list()\n", + " contrast_ax_ylim_tickintervals = list()\n", + " contrast_axes = axins\n", + " rawdata_axes.contrast_axes = axins\n", + "\n", + " else:\n", + " inset_contrast = False\n", + " # Here, we hardcode some figure parameters.\n", + " if float_contrast is True:\n", + " fig, axx = plt.subplots(\n", + " ncols=2,\n", + " gridspec_kw={\"width_ratios\": width_ratios_ga,\n", + " \"wspace\": 0},\n", + " **init_fig_kwargs)\n", + " fig.patch.set_facecolor(face_color)\n", + "\n", + " else:\n", + " fig, axx = plt.subplots(nrows=2,\n", + " gridspec_kw={\"hspace\": 0.3},\n", + " **init_fig_kwargs)\n", + " fig.patch.set_facecolor(face_color)\n", + " # If the contrast axes are NOT floating, create lists to store\n", + " # raw ylims and raw tick intervals, so that I can normalize\n", + " # their ylims later.\n", + " contrast_ax_ylim_low = list()\n", + " contrast_ax_ylim_high = list()\n", + " contrast_ax_ylim_tickintervals = list()\n", + "\n", + " rawdata_axes = axx[0]\n", + " contrast_axes = axx[1]\n", + " rawdata_axes.set_frame_on(False)\n", + " contrast_axes.set_frame_on(False)\n", + "\n", + " redraw_axes_kwargs = {'colors' : ytick_color,\n", + " 'facecolors' : ytick_color,\n", + " 'lw' : 1,\n", + " 'zorder' : 10,\n", + " 'clip_on' : False}\n", + "\n", + " swarm_ylim = plot_kwargs[\"swarm_ylim\"]\n", + "\n", + " if swarm_ylim is not None:\n", + " rawdata_axes.set_ylim(swarm_ylim)\n", + "\n", + " one_sankey = None\n", + " if is_paired is not None:\n", + " one_sankey = False # Flag to indicate if only one sankey is plotted.\n", + "\n", + " if show_pairs is True:\n", + " # Determine temp_idx based on is_paired and proportional conditions\n", + " if is_paired == \"baseline\":\n", + " idx_pairs = [(control, test) for i in idx for control, test in zip([i[0]] * (len(i) - 1), i[1:])]\n", + " temp_idx = idx if not proportional else idx_pairs\n", + " else:\n", + " idx_pairs = [(control, test) for i in idx for control, test in zip(i[:-1], i[1:])]\n", + " temp_idx = idx if not proportional else idx_pairs\n", + "\n", + " # Determine temp_all_plot_groups based on proportional condition\n", + " plot_groups = [item for i in temp_idx for item in i]\n", + " temp_all_plot_groups = all_plot_groups if not proportional else plot_groups\n", + " \n", + " if proportional==False:\n", + " # Plot the raw data as a slopegraph.\n", + " # Pivot the long (melted) data.\n", + " if color_col is None:\n", + " pivot_values = yvar\n", + " else:\n", + " pivot_values = [yvar, color_col]\n", + " pivoted_plot_data = pd.pivot(data=plot_data, index=dabest_obj.id_col,\n", + " columns=xvar, values=pivot_values)\n", + " x_start = 0\n", + " for ii, current_tuple in enumerate(temp_idx):\n", + " if len(temp_idx) > 1:\n", + " # Select only the data for the current tuple.\n", + " if color_col is None:\n", + " current_pair = pivoted_plot_data.reindex(columns=current_tuple)\n", + " else:\n", + " current_pair = pivoted_plot_data[yvar].reindex(columns=current_tuple)\n", + " else:\n", + " if color_col is None:\n", + " current_pair = pivoted_plot_data\n", + " else:\n", + " current_pair = pivoted_plot_data[yvar]\n", + " grp_count = len(current_tuple)\n", + " # Iterate through the data for the current tuple.\n", + " for ID, observation in current_pair.iterrows():\n", + " x_points = [t for t in range(x_start, x_start + grp_count)]\n", + " y_points = observation.tolist()\n", + "\n", + " if color_col is None:\n", + " slopegraph_kwargs['color'] = ytick_color\n", + " else:\n", + " color_key = pivoted_plot_data[color_col,\n", + " current_tuple[0]].loc[ID]\n", + " if isinstance(color_key, str) == True:\n", + " slopegraph_kwargs['color'] = plot_palette_raw[color_key]\n", + " slopegraph_kwargs['label'] = color_key\n", + "\n", + " rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)\n", + " x_start = x_start + grp_count\n", + " # Set the tick labels, because the slopegraph plotting doesn't.\n", + " rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))\n", + " rawdata_axes.set_xticklabels(temp_all_plot_groups)\n", + " \n", + " else:\n", + " # Plot the raw data as a set of Sankey Diagrams aligned like barplot.\n", + " group_summaries = plot_kwargs[\"group_summaries\"]\n", + " if group_summaries is None:\n", + " group_summaries = \"mean_sd\"\n", + " err_color = plot_kwargs[\"err_color\"]\n", + " if err_color == None:\n", + " err_color = \"black\"\n", + "\n", + " if show_pairs is True:\n", + " sankey_control_group = []\n", + " sankey_test_group = []\n", + " for i in temp_idx:\n", + " sankey_control_group.append(i[0])\n", + " sankey_test_group.append(i[1]) \n", + "\n", + " if len(temp_all_plot_groups) == 2:\n", + " one_sankey = True \n", + " \n", + " # Replace the paired proportional plot with sankey diagram\n", + " sankey = sankeydiag(plot_data, xvar=xvar, yvar=yvar, \n", + " left_idx=sankey_control_group, \n", + " right_idx=sankey_test_group,\n", + " palette=plot_palette_sankey,\n", + " ax=rawdata_axes, \n", + " one_sankey=one_sankey,\n", + " **sankey_kwargs)\n", + " \n", + " else:\n", + " if proportional==False:\n", + " # Plot the raw data as a swarmplot.\n", + " rawdata_plot = sns.swarmplot(data=plot_data, x=xvar, y=yvar,\n", + " ax=rawdata_axes,\n", + " order=all_plot_groups, hue=color_col,\n", + " palette=plot_palette_raw, zorder=1,\n", + " **swarmplot_kwargs)\n", + " else:\n", + " # Plot the raw data as a barplot.\n", + " bar1_df = pd.DataFrame({xvar: all_plot_groups, 'proportion': np.ones(len(all_plot_groups))})\n", + " bar1 = sns.barplot(data=bar1_df, x=xvar, y=\"proportion\",\n", + " ax=rawdata_axes,\n", + " order=all_plot_groups,\n", + " linewidth=2, facecolor=(1, 1, 1, 0), edgecolor=bar_color,\n", + " zorder=1)\n", + " bar2 = sns.barplot(data=plot_data, x=xvar, y=yvar,\n", + " ax=rawdata_axes,\n", + " order=all_plot_groups,\n", + " palette=plot_palette_bar,\n", + " zorder=1,\n", + " **barplot_kwargs)\n", + " # adjust the width of bars\n", + " bar_width = plot_kwargs[\"bar_width\"]\n", + " for bar in bar1.patches:\n", + " x = bar.get_x()\n", + " width = bar.get_width()\n", + " centre = x + width / 2.\n", + " bar.set_x(centre - bar_width / 2.)\n", + " bar.set_width(bar_width)\n", + "\n", + " # Plot the gapped line summaries, if this is not a Cumming plot.\n", + " # Also, we will not plot gapped lines for paired plots. For now.\n", + " group_summaries = plot_kwargs[\"group_summaries\"]\n", + " if group_summaries is None:\n", + " group_summaries = \"mean_sd\"\n", + "\n", + " if group_summaries is not None and proportional==False:\n", + " # Create list to gather xspans.\n", + " xspans = []\n", + " line_colors = []\n", + " for jj, c in enumerate(rawdata_axes.collections):\n", + " try:\n", + " _, x_max, _, _ = get_swarm_spans(c)\n", + " x_max_span = x_max - jj\n", + " xspans.append(x_max_span)\n", + " except TypeError:\n", + " # we have got a None, so skip and move on.\n", + " pass\n", + "\n", + " if bootstraps_color_by_group is True:\n", + " line_colors.append(plot_palette_raw[all_plot_groups[jj]])\n", + "\n", + " if len(line_colors) != len(all_plot_groups):\n", + " line_colors = ytick_color\n", + "\n", + " error_bar(plot_data, x=xvar, y=yvar,\n", + " # Hardcoded offset...\n", + " offset=xspans + np.array(plot_kwargs[\"group_summaries_offset\"]),\n", + " line_color=line_colors,\n", + " gap_width_percent=1.5,\n", + " type=group_summaries, ax=rawdata_axes,\n", + " method=\"gapped_lines\",\n", + " **group_summary_kwargs)\n", + "\n", + " if group_summaries is not None and proportional == True:\n", + "\n", + " err_color = plot_kwargs[\"err_color\"]\n", + " if err_color == None:\n", + " err_color = \"black\"\n", + " error_bar(plot_data, x=xvar, y=yvar,\n", + " offset=0,\n", + " line_color=err_color,\n", + " gap_width_percent=1.5,\n", + " type=group_summaries, ax=rawdata_axes,\n", + " method=\"proportional_error_bar\",\n", + " **group_summary_kwargs)\n", + "\n", + " # Add the counts to the rawdata axes xticks.\n", + " counts = plot_data.groupby(xvar).count()[yvar]\n", + " ticks_with_counts = []\n", + " for xticklab in rawdata_axes.xaxis.get_ticklabels():\n", + " t = xticklab.get_text()\n", + " if t.rfind(\"\\n\") != -1:\n", + " te = t[t.rfind(\"\\n\") + len(\"\\n\"):]\n", + " N = str(counts.loc[te])\n", + " te = t\n", + " else:\n", + " te = t\n", + " N = str(counts.loc[te])\n", + "\n", + " ticks_with_counts.append(\"{}\\nN = {}\".format(te, N))\n", + "\n", + " rawdata_axes.set_xticklabels(ticks_with_counts)\n", + "\n", + " # Save the handles and labels for the legend.\n", + " handles, labels = rawdata_axes.get_legend_handles_labels()\n", + " legend_labels = [l for l in labels]\n", + " legend_handles = [h for h in handles]\n", + " if bootstraps_color_by_group is False:\n", + " rawdata_axes.legend().set_visible(False)\n", + "\n", + " # Enforce the xtick of rawdata_axes to be 0 and 1 after drawing only one sankey\n", + " if one_sankey:\n", + " rawdata_axes.set_xticks([0, 1])\n", + "\n", + " # Plot effect sizes and bootstraps.\n", + " # Take note of where the `control` groups are.\n", + " if is_paired == \"baseline\" and show_pairs == True:\n", + " if proportional == True and one_sankey == False:\n", + " ticks_to_skip = []\n", + " ticks_to_plot = np.arange(0, len(temp_all_plot_groups)/2).tolist()\n", + " ticks_to_start_sankey = np.cumsum([len(i)-1 for i in idx]).tolist()\n", + " ticks_to_start_sankey.pop()\n", + " ticks_to_start_sankey.insert(0, 0)\n", + " else:\n", + " # ticks_to_skip = np.arange(0, len(temp_all_plot_groups), 2).tolist()\n", + " # ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist()\n", + " ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n", + " ticks_to_skip.insert(0, 0)\n", + " # Then obtain the ticks where we have to plot the effect sizes.\n", + " ticks_to_plot = [t for t in range(0, len(all_plot_groups))\n", + " if t not in ticks_to_skip]\n", + " ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist()\n", + " ticks_to_skip_contrast.insert(0, 0)\n", + " else:\n", + " if proportional == True and one_sankey == False:\n", + " ticks_to_skip = [len(sankey_control_group)]\n", + " # Then obtain the ticks where we have to plot the effect sizes.\n", + " ticks_to_plot = [t for t in range(0, len(temp_idx))\n", + " if t not in ticks_to_skip]\n", + " ticks_to_skip = []\n", + " ticks_to_start_sankey = np.cumsum([len(i)-1 for i in idx]).tolist()\n", + " ticks_to_start_sankey.pop()\n", + " ticks_to_start_sankey.insert(0, 0)\n", + " else:\n", + " ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n", + " ticks_to_skip.insert(0, 0)\n", + " # Then obtain the ticks where we have to plot the effect sizes.\n", + " ticks_to_plot = [t for t in range(0, len(all_plot_groups))\n", + " if t not in ticks_to_skip]\n", + "\n", + " # Plot the bootstraps, then the effect sizes and CIs.\n", + " es_marker_size = plot_kwargs[\"es_marker_size\"]\n", + " halfviolin_alpha = plot_kwargs[\"halfviolin_alpha\"]\n", + "\n", + " ci_type = plot_kwargs[\"ci_type\"]\n", + "\n", + " results = EffectSizeDataFrame.results\n", + " contrast_xtick_labels = []\n", + "\n", + "\n", + " for j, tick in enumerate(ticks_to_plot):\n", + " current_group = results.test[j]\n", + " current_control = results.control[j]\n", + " current_bootstrap = results.bootstraps[j]\n", + " current_effsize = results.difference[j]\n", + " if ci_type == \"bca\":\n", + " current_ci_low = results.bca_low[j]\n", + " current_ci_high = results.bca_high[j]\n", + " else:\n", + " current_ci_low = results.pct_low[j]\n", + " current_ci_high = results.pct_high[j]\n", + "\n", + "\n", + " # Create the violinplot.\n", + " # New in v0.2.6: drop negative infinities before plotting.\n", + " v = contrast_axes.violinplot(current_bootstrap[~np.isinf(current_bootstrap)],\n", + " positions=[tick],\n", + " **violinplot_kwargs)\n", + " # Turn the violinplot into half, and color it the same as the swarmplot.\n", + " # Do this only if the color column is not specified.\n", + " # Ideally, the alpha (transparency) fo the violin plot should be\n", + " # less than one so the effect size and CIs are visible.\n", + " if bootstraps_color_by_group is True:\n", + " fc = plot_palette_contrast[current_group]\n", + " else:\n", + " fc = \"grey\"\n", + "\n", + " halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)\n", + "\n", + " # Plot the effect size.\n", + " contrast_axes.plot([tick], current_effsize, marker='o',\n", + " color=ytick_color,\n", + " markersize=es_marker_size)\n", + " # Plot the confidence interval.\n", + " contrast_axes.plot([tick, tick],\n", + " [current_ci_low, current_ci_high],\n", + " linestyle=\"-\",\n", + " color=ytick_color,\n", + " linewidth=group_summary_kwargs['lw'])\n", + "\n", + " contrast_xtick_labels.append(\"{}\\nminus\\n{}\".format(current_group,\n", + " current_control))\n", + "\n", + " # Plot mini-meta violin\n", + " if show_mini_meta or show_delta2:\n", + " if show_mini_meta:\n", + " mini_meta_delta = EffectSizeDataFrame.mini_meta_delta\n", + " data = mini_meta_delta.bootstraps_weighted_delta\n", + " difference = mini_meta_delta.difference\n", + " if ci_type == \"bca\":\n", + " ci_low = mini_meta_delta.bca_low\n", + " ci_high = mini_meta_delta.bca_high\n", + " else:\n", + " ci_low = mini_meta_delta.pct_low\n", + " ci_high = mini_meta_delta.pct_high\n", + " else: \n", + " delta_delta = EffectSizeDataFrame.delta_delta\n", + " data = delta_delta.bootstraps_delta_delta\n", + " difference = delta_delta.difference\n", + " if ci_type == \"bca\":\n", + " ci_low = delta_delta.bca_low\n", + " ci_high = delta_delta.bca_high\n", + " else:\n", + " ci_low = delta_delta.pct_low\n", + " ci_high = delta_delta.pct_high\n", + " #Create the violinplot.\n", + " #New in v0.2.6: drop negative infinities before plotting.\n", + " position = max(rawdata_axes.get_xticks())+2\n", + " v = contrast_axes.violinplot(data[~np.isinf(data)],\n", + " positions=[position],\n", + " **violinplot_kwargs)\n", + "\n", + " fc = \"grey\"\n", + "\n", + " halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)\n", + "\n", + " # Plot the effect size.\n", + " contrast_axes.plot([position], difference, marker='o',\n", + " color=ytick_color,\n", + " markersize=es_marker_size)\n", + " # Plot the confidence interval.\n", + " contrast_axes.plot([position, position],\n", + " [ci_low, ci_high],\n", + " linestyle=\"-\",\n", + " color=ytick_color,\n", + " linewidth=group_summary_kwargs['lw'])\n", + " if show_mini_meta:\n", + " contrast_xtick_labels.extend([\"\",\"Weighted delta\"])\n", + " else:\n", + " contrast_xtick_labels.extend([\"\",\"delta-delta\"])\n", + "\n", + " # Make sure the contrast_axes x-lims match the rawdata_axes xlims,\n", + " # and add an extra violinplot tick for delta-delta plot.\n", + " if show_delta2 is False and show_mini_meta is False:\n", + " contrast_axes.set_xticks(rawdata_axes.get_xticks())\n", + " else:\n", + " temp = rawdata_axes.get_xticks()\n", + " temp = np.append(temp, [max(temp)+1, max(temp)+2])\n", + " contrast_axes.set_xticks(temp)\n", + "\n", + " if show_pairs is True:\n", + " max_x = contrast_axes.get_xlim()[1]\n", + " rawdata_axes.set_xlim(-0.375, max_x)\n", + "\n", + " if float_contrast is True:\n", + " contrast_axes.set_xlim(0.5, 1.5)\n", + " elif show_delta2 or show_mini_meta:\n", + " # Increase the xlim of raw data by 2\n", + " temp = rawdata_axes.get_xlim()\n", + " if show_pairs:\n", + " rawdata_axes.set_xlim(temp[0], temp[1]+0.25)\n", + " else:\n", + " rawdata_axes.set_xlim(temp[0], temp[1]+2)\n", + " contrast_axes.set_xlim(rawdata_axes.get_xlim())\n", + " else:\n", + " contrast_axes.set_xlim(rawdata_axes.get_xlim())\n", + "\n", + " # Properly label the contrast ticks.\n", + " for t in ticks_to_skip:\n", + " contrast_xtick_labels.insert(t, \"\")\n", + " \n", + " contrast_axes.set_xticklabels(contrast_xtick_labels)\n", + "\n", + " if bootstraps_color_by_group is False:\n", + " legend_labels_unique = np.unique(legend_labels)\n", + " unique_idx = np.unique(legend_labels, return_index=True)[1]\n", + " legend_handles_unique = (pd.Series(legend_handles, dtype=\"object\").loc[unique_idx]).tolist()\n", + "\n", + " if len(legend_handles_unique) > 0:\n", + " if float_contrast is True:\n", + " axes_with_legend = contrast_axes\n", + " if show_pairs is True:\n", + " bta = (1.75, 1.02)\n", + " else:\n", + " bta = (1.5, 1.02)\n", + " else:\n", + " axes_with_legend = rawdata_axes\n", + " if show_pairs is True:\n", + " bta = (1.02, 1.)\n", + " else:\n", + " bta = (1.,1.)\n", + " leg = axes_with_legend.legend(legend_handles_unique,\n", + " legend_labels_unique,\n", + " bbox_to_anchor=bta,\n", + " **legend_kwargs)\n", + " if show_pairs is True:\n", + " for line in leg.get_lines():\n", + " line.set_linewidth(3.0)\n", + "\n", + " og_ylim_raw = rawdata_axes.get_ylim()\n", + " og_xlim_raw = rawdata_axes.get_xlim()\n", + "\n", + " if float_contrast is True:\n", + " # For Gardner-Altman plots only.\n", + "\n", + " # Normalize ylims and despine the floating contrast axes.\n", + " # Check that the effect size is within the swarm ylims.\n", + " if effect_size_type in [\"mean_diff\", \"cohens_d\", \"hedges_g\",\"cohens_h\"]:\n", + " control_group_summary = plot_data.groupby(xvar)\\\n", + " .mean(numeric_only=True).loc[current_control, yvar]\n", + " test_group_summary = plot_data.groupby(xvar)\\\n", + " .mean(numeric_only=True).loc[current_group, yvar]\n", + " elif effect_size_type == \"median_diff\":\n", + " control_group_summary = plot_data.groupby(xvar)\\\n", + " .median().loc[current_control, yvar]\n", + " test_group_summary = plot_data.groupby(xvar)\\\n", + " .median().loc[current_group, yvar]\n", + "\n", + " if swarm_ylim is None:\n", + " swarm_ylim = rawdata_axes.get_ylim()\n", + "\n", + " _, contrast_xlim_max = contrast_axes.get_xlim()\n", + "\n", + " difference = float(results.difference[0])\n", + " \n", + " if effect_size_type in [\"mean_diff\", \"median_diff\"]:\n", + " # Align 0 of contrast_axes to reference group mean of rawdata_axes.\n", + " # If the effect size is positive, shift the contrast axis up.\n", + " rawdata_ylims = np.array(rawdata_axes.get_ylim())\n", + " if current_effsize > 0:\n", + " rightmin, rightmax = rawdata_ylims - current_effsize\n", + " # If the effect size is negative, shift the contrast axis down.\n", + " elif current_effsize < 0:\n", + " rightmin, rightmax = rawdata_ylims + current_effsize\n", + " else:\n", + " rightmin, rightmax = rawdata_ylims\n", + "\n", + " contrast_axes.set_ylim(rightmin, rightmax)\n", + "\n", + " og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary)\n", + "\n", + " contrast_axes.set_ylim(og_ylim_contrast)\n", + " contrast_axes.set_xlim(contrast_xlim_max-1, contrast_xlim_max)\n", + "\n", + " elif effect_size_type in [\"cohens_d\", \"hedges_g\",\"cohens_h\"]:\n", + " if is_paired:\n", + " which_std = 1\n", + " else:\n", + " which_std = 0\n", + " temp_control = plot_data[plot_data[xvar] == current_control][yvar]\n", + " temp_test = plot_data[plot_data[xvar] == current_group][yvar]\n", + " \n", + " stds = _compute_standardizers(temp_control, temp_test)\n", + " if is_paired:\n", + " pooled_sd = stds[1]\n", + " else:\n", + " pooled_sd = stds[0]\n", + " \n", + " if effect_size_type == 'hedges_g':\n", + " gby_count = plot_data.groupby(xvar).count()\n", + " len_control = gby_count.loc[current_control, yvar]\n", + " len_test = gby_count.loc[current_group, yvar]\n", + " \n", + " hg_correction_factor = _compute_hedges_correction_factor(len_control, len_test)\n", + " \n", + " ylim_scale_factor = pooled_sd / hg_correction_factor\n", + "\n", + " elif effect_size_type == \"cohens_h\":\n", + " ylim_scale_factor = (np.mean(temp_test)-np.mean(temp_control)) / difference\n", + "\n", + " else:\n", + " ylim_scale_factor = pooled_sd\n", + " \n", + " scaled_ylim = ((rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor).tolist()\n", + "\n", + " contrast_axes.set_ylim(scaled_ylim)\n", + " og_ylim_contrast = scaled_ylim\n", + "\n", + " contrast_axes.set_xlim(contrast_xlim_max-1, contrast_xlim_max)\n", + "\n", + " if one_sankey is None:\n", + " # Draw summary lines for control and test groups..\n", + " for jj, axx in enumerate([rawdata_axes, contrast_axes]):\n", + "\n", + " # Draw effect size line.\n", + " if jj == 0:\n", + " ref = control_group_summary\n", + " diff = test_group_summary\n", + " effsize_line_start = 1\n", + "\n", + " elif jj == 1:\n", + " ref = 0\n", + " diff = ref + difference\n", + " effsize_line_start = contrast_xlim_max-1.1\n", + "\n", + " xlimlow, xlimhigh = axx.get_xlim()\n", + "\n", + " # Draw reference line.\n", + " axx.hlines(ref, # y-coordinates\n", + " 0, xlimhigh, # x-coordinates, start and end.\n", + " **reflines_kwargs)\n", + " \n", + " # Draw effect size line.\n", + " axx.hlines(diff,\n", + " effsize_line_start, xlimhigh,\n", + " **reflines_kwargs)\n", + " else: \n", + " ref = 0\n", + " diff = ref + difference\n", + " effsize_line_start = contrast_xlim_max - 0.9\n", + " xlimlow, xlimhigh = contrast_axes.get_xlim()\n", + " # Draw reference line.\n", + " contrast_axes.hlines(ref, # y-coordinates\n", + " effsize_line_start, xlimhigh, # x-coordinates, start and end.\n", + " **reflines_kwargs)\n", + " \n", + " # Draw effect size line.\n", + " contrast_axes.hlines(diff,\n", + " effsize_line_start, xlimhigh,\n", + " **reflines_kwargs) \n", + " rawdata_axes.set_xlim(og_xlim_raw) # to align the axis\n", + " # Despine appropriately.\n", + " sns.despine(ax=rawdata_axes, bottom=True)\n", + " sns.despine(ax=contrast_axes, left=True, right=False)\n", + "\n", + " # Insert break between the rawdata axes and the contrast axes\n", + " # by re-drawing the x-spine.\n", + " rawdata_axes.hlines(og_ylim_raw[0], # yindex\n", + " rawdata_axes.get_xlim()[0], 1.3, # xmin, xmax\n", + " **redraw_axes_kwargs)\n", + " rawdata_axes.set_ylim(og_ylim_raw)\n", + "\n", + " contrast_axes.hlines(contrast_axes.get_ylim()[0],\n", + " contrast_xlim_max-0.8, contrast_xlim_max,\n", + " **redraw_axes_kwargs)\n", + "\n", + "\n", + " else:\n", + " # For Cumming Plots only.\n", + "\n", + " # Set custom contrast_ylim, if it was specified.\n", + " if plot_kwargs['contrast_ylim'] is not None or (plot_kwargs['delta2_ylim'] is not None and show_delta2):\n", + "\n", + " if plot_kwargs['contrast_ylim'] is not None:\n", + " custom_contrast_ylim = plot_kwargs['contrast_ylim']\n", + " if plot_kwargs['delta2_ylim'] is not None and show_delta2:\n", + " custom_delta2_ylim = plot_kwargs['delta2_ylim']\n", + " if custom_contrast_ylim!=custom_delta2_ylim:\n", + " err1 = \"Please check if `contrast_ylim` and `delta2_ylim` are assigned\"\n", + " err2 = \"with same values.\"\n", + " raise ValueError(err1 + err2)\n", + " else:\n", + " custom_delta2_ylim = plot_kwargs['delta2_ylim']\n", + " custom_contrast_ylim = custom_delta2_ylim\n", + "\n", + " if len(custom_contrast_ylim) != 2:\n", + " err1 = \"Please check `contrast_ylim` consists of \"\n", + " err2 = \"exactly two numbers.\"\n", + " raise ValueError(err1 + err2)\n", + "\n", + " if effect_size_type == \"cliffs_delta\":\n", + " # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1].\n", + " l = plot_kwargs['contrast_ylim'][0]\n", + " h = plot_kwargs['contrast_ylim'][1]\n", + " low = -1 if l < -1 else l\n", + " high = 1 if h > 1 else h\n", + " contrast_axes.set_ylim(low, high)\n", + " else:\n", + " contrast_axes.set_ylim(custom_contrast_ylim)\n", + "\n", + " # If 0 lies within the ylim of the contrast axes,\n", + " # draw a zero reference line.\n", + " contrast_axes_ylim = contrast_axes.get_ylim()\n", + " if contrast_axes_ylim[0] < contrast_axes_ylim[1]:\n", + " contrast_ylim_low, contrast_ylim_high = contrast_axes_ylim\n", + " else:\n", + " contrast_ylim_high, contrast_ylim_low = contrast_axes_ylim\n", + " if contrast_ylim_low < 0 < contrast_ylim_high:\n", + " contrast_axes.axhline(y=0, **reflines_kwargs)\n", + "\n", + " if is_paired == \"baseline\" and show_pairs == True:\n", + " if proportional == True and one_sankey == False:\n", + " rightend_ticks_raw = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey)\n", + " else: \n", + " rightend_ticks_raw = np.array([len(i)-1 for i in temp_idx]) + np.array(ticks_to_skip)\n", + " for ax in [rawdata_axes]:\n", + " sns.despine(ax=ax, bottom=True)\n", + " \n", + " ylim = ax.get_ylim()\n", + " xlim = ax.get_xlim()\n", + " redraw_axes_kwargs['y'] = ylim[0]\n", + " \n", + " if proportional == True and one_sankey == False:\n", + " for k, start_tick in enumerate(ticks_to_start_sankey):\n", + " end_tick = rightend_ticks_raw[k]\n", + " ax.hlines(xmin=start_tick, xmax=end_tick,\n", + " **redraw_axes_kwargs)\n", + " else: \n", + " for k, start_tick in enumerate(ticks_to_skip):\n", + " end_tick = rightend_ticks_raw[k]\n", + " ax.hlines(xmin=start_tick, xmax=end_tick,\n", + " **redraw_axes_kwargs)\n", + " ax.set_ylim(ylim)\n", + " del redraw_axes_kwargs['y']\n", + " \n", + " if proportional == False:\n", + " temp_length = [(len(i)-1) for i in idx]\n", + " else:\n", + " temp_length = [(len(i)-1)*2-1 for i in idx]\n", + " if proportional == True and one_sankey == False:\n", + " rightend_ticks_contrast = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey)\n", + " else: \n", + " rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)\n", + " for ax in [contrast_axes]:\n", + " sns.despine(ax=ax, bottom=True)\n", + " \n", + " ylim = ax.get_ylim()\n", + " xlim = ax.get_xlim()\n", + " redraw_axes_kwargs['y'] = ylim[0]\n", + " \n", + " if proportional == True and one_sankey == False:\n", + " for k, start_tick in enumerate(ticks_to_start_sankey):\n", + " end_tick = rightend_ticks_contrast[k]\n", + " ax.hlines(xmin=start_tick, xmax=end_tick,\n", + " **redraw_axes_kwargs)\n", + " else:\n", + " for k, start_tick in enumerate(ticks_to_skip_contrast):\n", + " end_tick = rightend_ticks_contrast[k]\n", + " ax.hlines(xmin=start_tick, xmax=end_tick,\n", + " **redraw_axes_kwargs) \n", + " \n", + " ax.set_ylim(ylim)\n", + " del redraw_axes_kwargs['y']\n", + " else:\n", + " # Compute the end of each x-axes line.\n", + " if proportional == True and one_sankey == False:\n", + " rightend_ticks = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey)\n", + " else:\n", + " rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip)\n", + " \n", + " for ax in [rawdata_axes, contrast_axes]:\n", + " sns.despine(ax=ax, bottom=True)\n", + " \n", + " ylim = ax.get_ylim()\n", + " xlim = ax.get_xlim()\n", + " redraw_axes_kwargs['y'] = ylim[0]\n", + " \n", + " if proportional == True and one_sankey == False:\n", + " for k, start_tick in enumerate(ticks_to_start_sankey):\n", + " end_tick = rightend_ticks[k]\n", + " ax.hlines(xmin=start_tick, xmax=end_tick,\n", + " **redraw_axes_kwargs)\n", + " else:\n", + " for k, start_tick in enumerate(ticks_to_skip):\n", + " end_tick = rightend_ticks[k]\n", + " ax.hlines(xmin=start_tick, xmax=end_tick,\n", + " **redraw_axes_kwargs)\n", + " \n", + " ax.set_ylim(ylim)\n", + " del redraw_axes_kwargs['y']\n", + "\n", + " if show_delta2 is True or show_mini_meta is True:\n", + " ylim = contrast_axes.get_ylim()\n", + " redraw_axes_kwargs['y'] = ylim[0]\n", + " x_ticks = contrast_axes.get_xticks()\n", + " contrast_axes.hlines(xmin=x_ticks[-2], xmax=x_ticks[-1],\n", + " **redraw_axes_kwargs)\n", + " del redraw_axes_kwargs['y']\n", + "\n", + " # Set raw axes y-label.\n", + " swarm_label = plot_kwargs['swarm_label']\n", + " if swarm_label is None and yvar is None:\n", + " swarm_label = \"value\"\n", + " elif swarm_label is None and yvar is not None:\n", + " swarm_label = yvar\n", + "\n", + " bar_label = plot_kwargs['bar_label']\n", + " if bar_label is None and effect_size_type != \"cohens_h\":\n", + " bar_label = \"proportion of success\"\n", + " elif bar_label is None and effect_size_type == \"cohens_h\":\n", + " bar_label = \"value\"\n", + "\n", + " # Place contrast axes y-label.\n", + " contrast_label_dict = {'mean_diff': \"mean difference\",\n", + " 'median_diff': \"median difference\",\n", + " 'cohens_d': \"Cohen's d\",\n", + " 'hedges_g': \"Hedges' g\",\n", + " 'cliffs_delta': \"Cliff's delta\",\n", + " 'cohens_h': \"Cohen's h\"}\n", + "\n", + " if proportional == True and effect_size_type != \"cohens_h\":\n", + " default_contrast_label = \"proportion difference\"\n", + " else:\n", + " default_contrast_label = contrast_label_dict[EffectSizeDataFrame.effect_size]\n", + "\n", + "\n", + " if plot_kwargs['contrast_label'] is None:\n", + " if is_paired:\n", + " contrast_label = \"paired\\n{}\".format(default_contrast_label)\n", + " else:\n", + " contrast_label = default_contrast_label\n", + " contrast_label = contrast_label.capitalize()\n", + " else:\n", + " contrast_label = plot_kwargs['contrast_label']\n", + "\n", + " contrast_axes.set_ylabel(contrast_label)\n", + " if float_contrast is True:\n", + " contrast_axes.yaxis.set_label_position(\"right\")\n", + "\n", + " # Set the rawdata axes labels appropriately\n", + " if proportional == False:\n", + " rawdata_axes.set_ylabel(swarm_label)\n", + " else:\n", + " rawdata_axes.set_ylabel(bar_label)\n", + " rawdata_axes.set_xlabel(\"\")\n", + "\n", + " # Because we turned the axes frame off, we also need to draw back\n", + " # the y-spine for both axes.\n", + " if float_contrast==False:\n", + " rawdata_axes.set_xlim(contrast_axes.get_xlim())\n", + " og_xlim_raw = rawdata_axes.get_xlim()\n", + " rawdata_axes.vlines(og_xlim_raw[0],\n", + " og_ylim_raw[0], og_ylim_raw[1],\n", + " **redraw_axes_kwargs)\n", + "\n", + " og_xlim_contrast = contrast_axes.get_xlim()\n", + "\n", + " if float_contrast is True:\n", + " xpos = og_xlim_contrast[1]\n", + " else:\n", + " xpos = og_xlim_contrast[0]\n", + "\n", + " og_ylim_contrast = contrast_axes.get_ylim()\n", + " contrast_axes.vlines(xpos,\n", + " og_ylim_contrast[0], og_ylim_contrast[1],\n", + " **redraw_axes_kwargs)\n", + "\n", + "\n", + " if show_delta2 is True:\n", + " if plot_kwargs['delta2_label'] is None:\n", + " delta2_label = \"delta - delta\"\n", + " else: \n", + " delta2_label = plot_kwargs['delta2_label']\n", + " delta2_axes = contrast_axes.twinx()\n", + " delta2_axes.set_frame_on(False)\n", + " delta2_axes.set_ylabel(delta2_label)\n", + " og_xlim_delta = contrast_axes.get_xlim()\n", + " og_ylim_delta = contrast_axes.get_ylim()\n", + " delta2_axes.set_ylim(og_ylim_delta)\n", + " delta2_axes.vlines(og_xlim_delta[1],\n", + " og_ylim_delta[0], og_ylim_delta[1],\n", + " **redraw_axes_kwargs)\n", + "\n", + " # Make sure no stray ticks appear!\n", + " rawdata_axes.xaxis.set_ticks_position('bottom')\n", + " rawdata_axes.yaxis.set_ticks_position('left')\n", + " contrast_axes.xaxis.set_ticks_position('bottom')\n", + " if float_contrast is False:\n", + " contrast_axes.yaxis.set_ticks_position('left')\n", + "\n", + " # Reset rcParams.\n", + " for parameter in _changed_rcParams:\n", + " plt.rcParams[parameter] = original_rcParams[parameter]\n", + "\n", + " # Return the figure.\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d02b55f2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml new file mode 100644 index 00000000..6fcc349d --- /dev/null +++ b/nbs/_quarto.yml @@ -0,0 +1,46 @@ +project: + type: website + +format: + html: + theme: cosmo + css: styles.css + toc: true + toc-depth: 4 + +website: + twitter-card: true + open-graph: true + repo-actions: [issue] + sidebar: + style: floating + contents: + - auto: "/*.ipynb" + - section: Tutorials + contents: tutorials/* + - section: API + contents: API/* + navbar: + background: primary + search: true + collapse-below: lg + left: + - text: "Get Started" + href: 01-getting_started.ipynb + - text: "Tutorial" + href: tutorials/index.qmd + - text: "Blog" + href: blog/index.qmd + - text: "Help" + menu: + - text: "Report an Issue" + icon: bug + href: https://github.com/ACCLAB/DABEST-python/issues + right: + - icon: github + href: "https://github.com/ACCLAB/DABEST-python" + - icon: twitter + href: https://twitter.com/EstimationStats + aria-label: ACCLAB Twitter + +metadata-files: [nbdev.yml, sidebar.yml] \ No newline at end of file diff --git a/docs/source/_static/css/custom.css b/nbs/_static/css/custom.css similarity index 100% rename from docs/source/_static/css/custom.css rename to nbs/_static/css/custom.css diff --git a/docs/source/_static/four_samples.csv b/nbs/_static/four_samples.csv similarity index 100% rename from docs/source/_static/four_samples.csv rename to nbs/_static/four_samples.csv diff --git a/nbs/blog/index.qmd b/nbs/blog/index.qmd new file mode 100644 index 00000000..90f738e4 --- /dev/null +++ b/nbs/blog/index.qmd @@ -0,0 +1,12 @@ +--- +title: DABEST Blog +subtitle: News, tips, and commentary about all things dabest +listing: + sort: "date desc" + contents: "posts" + sort-ui: false + filter-ui: false + categories: true + feed: true +page-layout: full +--- \ No newline at end of file diff --git a/docs/source/_images/bootstrap-1.png b/nbs/blog/posts/bootstraps/bootstrap-1.png similarity index 100% rename from docs/source/_images/bootstrap-1.png rename to nbs/blog/posts/bootstraps/bootstrap-1.png diff --git a/docs/source/_images/bootstrap-2.png b/nbs/blog/posts/bootstraps/bootstrap-2.png similarity index 100% rename from docs/source/_images/bootstrap-2.png rename to nbs/blog/posts/bootstraps/bootstrap-2.png diff --git a/docs/source/_images/bootstrap-3.png b/nbs/blog/posts/bootstraps/bootstrap-3.png similarity index 100% rename from docs/source/_images/bootstrap-3.png rename to nbs/blog/posts/bootstraps/bootstrap-3.png diff --git a/docs/source/_images/bootstrap-4.png b/nbs/blog/posts/bootstraps/bootstrap-4.png similarity index 100% rename from docs/source/_images/bootstrap-4.png rename to nbs/blog/posts/bootstraps/bootstrap-4.png diff --git a/docs/source/_images/bootstrap-5.png b/nbs/blog/posts/bootstraps/bootstrap-5.png similarity index 100% rename from docs/source/_images/bootstrap-5.png rename to nbs/blog/posts/bootstraps/bootstrap-5.png diff --git a/nbs/blog/posts/bootstraps/bootstraps.ipynb b/nbs/blog/posts/bootstraps/bootstraps.ipynb new file mode 100644 index 00000000..ae4ff5ad --- /dev/null +++ b/nbs/blog/posts/bootstraps/bootstraps.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1a3ec507", + "metadata": {}, + "source": [ + "# Bootstrap Confidence Intervals\n", + "\n", + "> Explaination of the bootstrap method and its application in hypothesis testing using DABEST.\n", + "\n", + "- order: 3" + ] + }, + { + "cell_type": "markdown", + "id": "6321ea6f", + "metadata": {}, + "source": [ + "## Sampling from Populations" + ] + }, + { + "cell_type": "markdown", + "id": "49954f18", + "metadata": {}, + "source": [ + "In a typical scientific experiment, we are interested in two populations\n", + "(Control and Test), and whether there is a difference between their means\n", + "$(\\mu_{Test}-\\mu_{Control})$\n" + ] + }, + { + "cell_type": "markdown", + "id": "e12b893f", + "metadata": {}, + "source": [ + "![](bootstrap-1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "5573045c", + "metadata": {}, + "source": [ + "We go about this by collecting observations from the control population, and from the test population." + ] + }, + { + "cell_type": "markdown", + "id": "359a36fb", + "metadata": {}, + "source": [ + "![](bootstrap-2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "786aa6c8", + "metadata": {}, + "source": [ + "We can easily compute the mean difference in our observed samples. This is our\n", + "estimate of the population effect size that we are interested in.\n", + "\n", + "**But how do we obtain a measure of precision and confidence about our estimate?\n", + "Can we get a sense of how it relates to the population mean difference?**\n" + ] + }, + { + "cell_type": "markdown", + "id": "bfadcada", + "metadata": {}, + "source": [ + "## The bootstrap confidence interval" + ] + }, + { + "cell_type": "markdown", + "id": "fe977cc6", + "metadata": {}, + "source": [ + "We want to obtain a 95% confidence interval (95% CI) around the our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.\n", + "\n", + "In other words, if we repeated our experiment 100 times, gathering 100 independent sets of observations, and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.\n", + "\n", + "We can calculate the 95% CI of the mean difference with [bootstrap resampling](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))\n" + ] + }, + { + "cell_type": "markdown", + "id": "b9d76d7f", + "metadata": {}, + "source": [ + "### The bootstrap in action" + ] + }, + { + "cell_type": "markdown", + "id": "0685adaf", + "metadata": {}, + "source": [ + "The [`bootstrap`](#1)[1] is a simple but powerful technique. It was [first described] (https://projecteuclid.org/euclid.aos/1176344552) by [Bradley Efron](https://statistics.stanford.edu/people/bradley-efron).\n", + "\n", + "It creates multiple *resamples* (with replacement) from a single set of\n", + "observations, and computes the effect size of interest on each of these\n", + "resamples. The bootstrap resamples of the effect size can then be used to\n", + "determine the 95% CI.\n", + "\n", + "With computers, we can perform 5000 resamples very easily." + ] + }, + { + "cell_type": "markdown", + "id": "87e785c4", + "metadata": {}, + "source": [ + "![](bootstrap-3.png)" + ] + }, + { + "cell_type": "markdown", + "id": "0b68ae9c", + "metadata": {}, + "source": [ + "The resampling distribution of the difference in means approaches a normal\n", + "distribution. This is due to the [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem): a large number of\n", + "independent random samples will approach a normal distribution even if the\n", + "underlying population is not normally distributed.\n", + "\n", + "Bootstrap resampling gives us two important benefits:\n", + "\n", + "1. *Non-parametric statistical analysis.* There is no need to assume that our\n", + "observations, or the underlying populations, are normally distributed. Thanks to\n", + "the Central Limit Theorem, the resampling distribution of the effect size will\n", + "approach a normality.\n", + "\n", + "2. *Easy construction of the 95% CI from the resampling distribution.* For 1000\n", + "bootstrap resamples of the mean difference, one can use the 25th value and the\n", + "975th value of the ranked differences as boundaries of the 95% confidence\n", + "interval. (This captures the central 95% of the distribution.) Such an interval\n", + "construction is known as a *percentile interval*." + ] + }, + { + "cell_type": "markdown", + "id": "cc5b9fa3", + "metadata": {}, + "source": [ + "## Adjusting for asymmetrical resampling distributions" + ] + }, + { + "cell_type": "markdown", + "id": "e83634e9", + "metadata": {}, + "source": [ + "While resampling distributions of the difference in means often have a normal\n", + "distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron\n", + "developed the [bias-corrected and accelerated bootstrap]\n", + "(https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#History) (BCa\n", + "bootstrap) to account for the skew, and still obtain the central 95% of the\n", + "distribution.\n", + "\n", + "DABEST applies the BCa correction to the resampling bootstrap distributions of\n", + "the effect size." + ] + }, + { + "cell_type": "markdown", + "id": "88ab2684", + "metadata": {}, + "source": [ + "![](bootstrap-4.png)" + ] + }, + { + "cell_type": "markdown", + "id": "c1f02904", + "metadata": {}, + "source": [ + "## Estimation plots incorporate bootstrap resampling" + ] + }, + { + "cell_type": "markdown", + "id": "fb1a8fa6", + "metadata": {}, + "source": [ + "The estimation plot produced by DABEST presents the rawdata and the bootstrap\n", + "confidence interval of the effect size (the difference in means) side-by-side as\n", + "a single integrated plot." + ] + }, + { + "cell_type": "markdown", + "id": "418f5fb4", + "metadata": {}, + "source": [ + "![](bootstrap-5.png)" + ] + }, + { + "cell_type": "markdown", + "id": "eaad7dd5", + "metadata": {}, + "source": [ + "It thus tightly couples visual presentation of the raw data with an indication of the population mean difference, and its confidence interval." + ] + }, + { + "cell_type": "markdown", + "id": "c540cd14", + "metadata": {}, + "source": [ + "\n", + "`[1]`: The name is derived from the saying \"[pull oneself by one's bootstraps](https://en.wiktionary.org/wiki/pull_oneself_up_by_one%27s_bootstraps)\", often used as an exhortation to achieve success without external help.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87e5611b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb new file mode 100644 index 00000000..200260d1 --- /dev/null +++ b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb @@ -0,0 +1,424 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5b2f1637", + "metadata": {}, + "source": [ + "# Robust and Beautiful Statistical Visualization\n", + "\n", + "- order: 3" + ] + }, + { + "cell_type": "markdown", + "id": "f086f2b7", + "metadata": {}, + "source": [ + "## Current plots do not work" + ] + }, + { + "cell_type": "markdown", + "id": "3146c6cf", + "metadata": {}, + "source": [ + "What is data visualization? Battle-Baptiste and Rusert (2018) give a\n", + "cogent and compelling definition:\n", + "\n", + " [`Data visualization`](#1)[1] is the rendering of information in a visual\n", + " format to help communicate data while also generating new patterns\n", + " and knowledge through the act of visualization itself. \n", + "\n", + "Sadly, too many figures and visualizations in modern academic\n", + "publications seemingly fail to \"generate new patterns and knowledge\n", + "through the act of visualization itself\". Here, we propose a solution:\n", + "*the estimation plot*." + ] + }, + { + "cell_type": "markdown", + "id": "d30acb26", + "metadata": {}, + "source": [ + "### The barplot conceals the underlying shape" + ] + }, + { + "cell_type": "markdown", + "id": "c5fee24b", + "metadata": {}, + "source": [ + "By only displaying the mean and standard deviation, barplots do not\n", + "accurately represent the underlying distribution of the data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "9e1ffa32", + "metadata": {}, + "source": [ + "![](robust_5_1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "21f7b0cf", + "metadata": {}, + "source": [ + "In the above figure, four different samples with wildly different\n", + "distributions--as seen in the swarmplot on the left panel--look exactly\n", + "the same when visualized with a barplot on the right panel. (You can\n", + "download the [dataset](_static/four_samples.csv) to see for yourself.)\n", + "\n", + "We're not the first ones (see\n", + "[this](https://www.nature.com/articles/nmeth.2837),\n", + "[this](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128),\n", + "or\n", + "[that](https://onlinelibrary.wiley.com/doi/full/10.1111/ejn.13400))\n", + "to point out the barplot's fatal flaws. Indeed, it is both sobering and\n", + "fascinating to realise that the barplot is a [17th century\n", + "invention](https://en.wikipedia.org/wiki/Bar_chart#History) initially\n", + "used to compare single values, not to compare summarized and aggregated\n", + "data. " + ] + }, + { + "cell_type": "markdown", + "id": "a9be5a36", + "metadata": {}, + "source": [ + "### The boxplot does not convey sample size" + ] + }, + { + "cell_type": "markdown", + "id": "0efd5b25", + "metadata": {}, + "source": [ + "Boxplots are another widely used visualization tool. They arguably do\n", + "include more information for each sample (medians, quartiles, maxima,\n", + "minima, and outliers), but they do not convey to the viewer the size of\n", + "each sample." + ] + }, + { + "cell_type": "markdown", + "id": "9bff917e", + "metadata": {}, + "source": [ + "![](robust_7_1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "7cd5dc07", + "metadata": {}, + "source": [ + "The figure above visualizes the same four samples as a swarmplot (left\n", + "panel) and as a boxplot. If we did not label the x-axis with the sample\n", + "size, it would be impossible to definitively distinguish the sample with\n", + "5 obesrvations from the sample with 50.\n", + "\n", + "Even if the world gets rid of barplots and boxplots, the problems\n", + "plaguing statistical practices will remain unsolved. Null-hypothesis\n", + "significance testing--the dominant statistical paradigm in basic\n", + "research--does not indicate the **effect size**, or its **confidence\n", + "interval**." + ] + }, + { + "cell_type": "markdown", + "id": "3d2c600c", + "metadata": {}, + "source": [ + "## Introducing the Estimation Plot" + ] + }, + { + "cell_type": "markdown", + "id": "41652f5d", + "metadata": {}, + "source": [ + "![](robust_9_0.png)" + ] + }, + { + "cell_type": "markdown", + "id": "a7e3b1ad", + "metadata": {}, + "source": [ + "hown above is a Gardner-Altman estimation plot. (The plot draws its name from\n", + "[Martin J. Gardner]\n", + "(https://www.independent.co.uk/news/people/obituary-professor-martin-gardner-1470261.html)\n", + "and [Douglas Altman](https://www.bmj.com/content/361/bmj.k2588), who are\n", + "credited with [creating the design]\n", + "(https://www.bmj.com/content/bmj/292/6522/746.full.pdf) in 1986).\n", + "\n", + "This plot has two key features:\n", + "\n", + " 1. It presents all datapoints as a *swarmplot*, which orders each point to\n", + " display the underlying distribution.\n", + "\n", + " 2. It presents the effect size as a *bootstrap 95% confidence interval* (95% CI)\n", + " on a separate but aligned axes. where the effect size is displayed to the right\n", + " of the war data, and the mean of the test group is aligned with the effect size.\n", + "\n", + "*Thus, estimation plots are robust, beautiful, and convey important statistical\n", + "information elegantly and efficiently.*\n", + "\n", + "An estimation plot obtains and displays the 95% CI through nonparametric\n", + "bootstrap resampling. This enables visualization of the confidence interval as\n", + "a graded sampling distribution.\n", + "\n", + "This is one important difference between estimation plots created by DABEST, and\n", + "the original Gardner-Altman design. Here, the 95% CI is computed through\n", + "parametric methods, and displayed as a vertical error bar.\n", + "\n", + "Read more about this technique at [bootstraps](03-bootstraps.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "fe2e12b5", + "metadata": {}, + "source": [ + "### Introducing Estimation Statistics" + ] + }, + { + "cell_type": "markdown", + "id": "e21d785b", + "metadata": {}, + "source": [ + "Estimation plots emerge from *estimation statistics*, a simple\n", + "[framework](https://thenewstatistics.com/itns/) that avoids the\n", + "[pitfalls of significance\n", + "testing](https://www.nature.com/articles/nmeth.3288). It focuses on\n", + "the effect sizes of one's experiment/interventions, and uses familiar\n", + "statistical concepts: means, mean differences, and error bars.\n", + "\n", + "Significance testing calculates the probability (the *P* value) that the\n", + "experimental data would be observed, if the intervention did not produce\n", + "a change in the metric measured (i.e. the null hypothesis). This leads\n", + "analysts to apply a false dichotomy on the experimental intervention.\n", + "\n", + "Estimation statistics, on the other hand, focuses on the magnitude of\n", + "the effect (the effect size) and its precision. This encourages analysts\n", + "to gain a deeper understanding of the metrics used, and how they relate\n", + "to the natural processes being studied." + ] + }, + { + "cell_type": "markdown", + "id": "36e0fe1e", + "metadata": {}, + "source": [ + "## An Estimation Plot For Every Experimental Design" + ] + }, + { + "cell_type": "markdown", + "id": "d2668b9c", + "metadata": {}, + "source": [ + "For each of the most routine significance tests, there is an estimation\n", + "replacement:" + ] + }, + { + "cell_type": "markdown", + "id": "ab4458b8", + "metadata": {}, + "source": [ + "### Unpaired Student's t-test --> Two-group estimation plot" + ] + }, + { + "cell_type": "markdown", + "id": "af5b3ccb", + "metadata": {}, + "source": [ + "![](robust_12_0.png)" + ] + }, + { + "cell_type": "markdown", + "id": "e408503b", + "metadata": {}, + "source": [ + "### Paired Student's t-test --> Paired estimation plot" + ] + }, + { + "cell_type": "markdown", + "id": "6998d765", + "metadata": {}, + "source": [ + "\n", + "The Gardner-Altman estimation plot can also display effect sizes for\n", + "repeated measures (aka a paired experimental design) using a [Tufte\n", + "slopegraph](http://charliepark.org/slopegraphs/) instead of a\n", + "swarmplot." + ] + }, + { + "cell_type": "markdown", + "id": "fdb34170", + "metadata": {}, + "source": [ + "![](robust_14_0.png)" + ] + }, + { + "cell_type": "markdown", + "id": "6e6fd77d", + "metadata": {}, + "source": [ + "### One-way ANOVA + multiple comparisons --> Multi two-group estimation plot" + ] + }, + { + "cell_type": "markdown", + "id": "b7b643f8", + "metadata": {}, + "source": [ + "For comparisons between 3 or more groups that typically employ analysis\n", + "of variance (ANOVA) methods, one can use the [Cumming estimation\n", + "plot](https://en.wikipedia.org/wiki/Estimation_statistics#Cumming_plot),\n", + "named after [Geoff\n", + "Cumming](https://www.youtube.com/watch?v=nDN-hcKR7j8), and draws its\n", + "design heavily from his 2012 textbook [Understanding the New\n", + "Statistics](https://www.routledge.com/Understanding-The-New-Statistics-Effect-Sizes-Confidence-Intervals-and/Cumming/p/book/9780415879682).\n", + "This estimation plot design can be considered a variant of the\n", + "Gardner-Altman plot.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3f38c1fe", + "metadata": {}, + "source": [ + "![](robust_16_0.png)" + ] + }, + { + "cell_type": "markdown", + "id": "b443b0a8", + "metadata": {}, + "source": [ + "The effect size and 95% CIs are still plotted a separate axes, but\n", + "unlike the Gardner-Altman plot, this axes is positioned beneath the raw\n", + "data.\n", + "\n", + "Such a design frees up visual space in the upper panel, allowing the\n", + "display of summary measurements (mean ± standard deviation) for each\n", + "group. These are shown as gapped lines to the right of each group. The\n", + "mean of each group is indicated as a gap in the line, adhering to Edward\n", + "Tufte's dictum to [keep the data-ink ratio\n", + "low](https://medium.com/@plotlygraphs/maximizing-the-data-ink-ratio-in-dashboards-and-slide-deck-7887f7c1fab).\n" + ] + }, + { + "cell_type": "markdown", + "id": "da932b0e", + "metadata": {}, + "source": [ + "### Repeated measures ANOVA --> Multi paired estimation plot" + ] + }, + { + "cell_type": "markdown", + "id": "f0442420", + "metadata": {}, + "source": [ + "![](robust_19_0.png)" + ] + }, + { + "cell_type": "markdown", + "id": "2769a16c", + "metadata": {}, + "source": [ + "### Ordered groups ANOVA --> Shared-control estimation plot" + ] + }, + { + "cell_type": "markdown", + "id": "6eb943a9", + "metadata": {}, + "source": [ + "![](robust_21_0.png)" + ] + }, + { + "cell_type": "markdown", + "id": "7bde79f4", + "metadata": {}, + "source": [ + "### Estimation Plots: The Way Forward" + ] + }, + { + "cell_type": "markdown", + "id": "2e221c93", + "metadata": {}, + "source": [ + "In summary, estimation plots offer five key benefits relative to\n", + "conventional plots:" + ] + }, + { + "cell_type": "markdown", + "id": "fddf7595", + "metadata": {}, + "source": [ + "| | Barplot | Boxplot | Estimation Plot |\n", + "|---------------------|---------|---------|----------------|\n", + "| Displays all observed values | NO | NO | Yes |\n", + "| Avoids false dichotomy | NO | NO | Yes |\n", + "| Focusses on effect size | NO | NO | Yes |\n", + "| Visualizes effect size precision | NO | NO | Yes |\n", + "| Shows mean difference distribution | NO | NO | Yes |" + ] + }, + { + "cell_type": "markdown", + "id": "714f26b5", + "metadata": {}, + "source": [ + "You can create estimation plots using the DABEST (Data Analysis with\n", + "Bootstrap Estimation) packages, which are available in\n", + "[Matlab](https://github.com/ACCLAB/DABEST-Matlab),\n", + "[Python](https://github.com/ACCLAB/DABEST-python), and\n", + "[R](https://github.com/ACCLAB/dabestr)." + ] + }, + { + "cell_type": "markdown", + "id": "c55bb639", + "metadata": {}, + "source": [ + "\n", + "`[1]`:[W. E. B. Du Bois's Data Portraits: Visualizing Black America](https://www.papress.com/html/product.details.dna?isbn=9781616897062). Edited by Whitney Battle-Baptiste and Britt Rusert, Princeton Architectural Press, 2018\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a43239a3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/_images/robust_12_0.png b/nbs/blog/posts/robust-beautiful/robust_12_0.png similarity index 100% rename from docs/source/_images/robust_12_0.png rename to nbs/blog/posts/robust-beautiful/robust_12_0.png diff --git a/docs/source/_images/robust_14_0.png b/nbs/blog/posts/robust-beautiful/robust_14_0.png similarity index 100% rename from docs/source/_images/robust_14_0.png rename to nbs/blog/posts/robust-beautiful/robust_14_0.png diff --git a/docs/source/_images/robust_16_0.png b/nbs/blog/posts/robust-beautiful/robust_16_0.png similarity index 100% rename from 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+#title-block-header { + display: none; +} + +.navbar { + background: var(--background); + text-align: left; + font-size: 16px; +} + +.title, .navbar-title { + font-weight: bold; + padding: 4px 8px; + border-radius: 8px; + margin-right: 4px; +} + +.navbar-title { + background: linear-gradient(240.01deg, #4B555B 15.29%, rgba(67, 78, 84, 0) 138.75%); +} + +.title { + background: linear-gradient(240.01deg, #78858C 15.29%, rgba(105, 117, 123, 0) 138.75%); +} + +.content-block { + margin-left: 30px; + margin-right: 30px; + overflow-x: hidden; +} + +@media(min-width: 900px) { + .content-block { + margin-left: 50px; + margin-right: 50px; + } +} + +@media(min-width: 1200px) { + .content-block { + max-width: var(--content-width); + margin-left: auto; + margin-right: auto; + } +} + +.hero-banner { + padding-top: 20px; + padding-bottom: 10px; + margin-bottom: 30px; + display: flex; + flex-direction: column; + align-items: center; +} + +.hero-banner h1 { + font-size: 56px; + font-weight: bold; + margin-top: 10px; + margin-bottom: 40px; +} + +.hero-banner h2 { + border: none; + font-size: 40px; + font-weight: bold; + margin-top: 10px; + margin-bottom: 40px; +} + +.hero-banner h3 { + color: var(--foreground); + font-size: 20px; + max-width: 90%; + margin-left: auto; + margin-right: auto; + margin-top: 0; + margin-bottom: 40px; +} + +@media(min-width: 900px) { + .hero-banner h3 { + max-width: 60%; + } +} + +@media(min-width: 1200px) { + .hero-banner h3 { + max-width: 75%; + } +} + +.mid-content { + width: 100%; + padding: 0; + padding-top: 80px !important; + padding-bottom: 80px; + background: linear-gradient(235.87deg, #656A75 12.29%, #33373F 107.53%); + overflow-x: hidden; +} + +.testimonial { + background: rgba(51, 55, 63, 0.4); + border-radius: 12px; + padding: 30px; + text-align: left; +} + +.testimonial p { + margin: 0; + padding: 0; +} + +.testimonial img { + margin: 0; + padding: 0; + display: inline; + float: left; + width: 50px; + margin-right: 20px; + border-radius: 8px; +} + +.testimonial h1 { + font-size: 16px; + font-weight: bold; + padding: 0; + margin: 0; + margin-bottom: 4px; +} + +.testimonial h2 { + border: none; + font-size: 16px; + padding: 0; + margin: 0; + color: #A2A8B4; +} + +.testimonial h3 { + margin: 0; + margin-top: 30px; + font-size: 16px; +} + +.feature { + font-size: 16px; + height: 210px; + background: var(--background-2); + border-radius: 12px; + padding: 30px; + text-align: left; + display: flex; + flex-direction: column; +} + +.feature img { + margin-bottom: 20px; + width: 48px; +} + +.footer { + color: #A2A8B4; + font-size: 16px; + margin-top: 40px; + padding-top: 30px !important; + border-top: 1px solid var(--foreground-2); +} + +.btn-action-primary { + color: #F9FAFB; + background: #009AF1; + font-weight: 700; + line-height: 20px; /* identical to box height */ +} + +.btn-action-primary:hover, .btn-action-primary:active, .btn-action-primary:focus { + background: #35acf0; +} + +.btn-action { + min-width: 165px; + border-radius: 8px; + padding: 20px 48px; + border: none; +} + +/* Below are needed to make .ipynb equivalent to .qmd */ + +.figure { + display: inline; +} + +.quarto-figure { + margin: 0; +} diff --git a/nbs/index.qmd.py b/nbs/index.qmd.py new file mode 100644 index 00000000..7f8cf559 --- /dev/null +++ b/nbs/index.qmd.py @@ -0,0 +1,71 @@ +"""--- +title: Home +pagetitle: dabest +page-layout: custom +section-divs: false +css: index.css +toc: false +---""" + +from fastcore.foundation import L +from nbdev import qmd + +def img(fname, classes=None, **kwargs): return qmd.img(f"images/{fname}", classes=classes, **kwargs) +def btn(txt, link): return qmd.btn(txt, link=link, classes=['btn-action-primary', 'btn-action', 'btn', 'btn-success', 'btn-lg']) +def banner(txt, classes=None, style=None): return qmd.div(txt, L('hero-banner')+classes, style=style) + +features = L( + ('docs', 'Beautiful technical documentation and scientific articles with Quarto'), + ('testing', 'Out-of-the-box continuous integration with GitHub Actions'), + ('packaging', 'Publish code to PyPI and conda, and prose to GitHub Pages'), + ('visualization', 'Estimation plots are robust, beautiful, and convey important statistical information elegantly and efficiently.'), + ('jupyter', 'Write prose, code, and tests in notebooks'), + ('git', 'Git-friendly notebooks: human-readable merge conflicts') +) + +def industry(im, **kwargs): return qmd.div(img(im, **kwargs), ["g-col-12", "g-col-sm-6", "g-col-md-3"]) + +def testm(im, nm, detl, txt): + return qmd.div(f"""{img(im, link=True)} + +# {nm} + +## {detl} + +### {txt}""", ["testimonial", "g-col-12", "g-col-md-6"]) + + +def feature(im, desc): return qmd.div(f"{img(im+'.svg')}\n\n{desc}\n", ['feature', 'g-col-12', 'g-col-sm-6', 'g-col-md-4']) + +feature_d = qmd.div('\n'.join(features.starmap(feature)), ['grid', 'gap-4'], style={"padding-bottom": "60px"}) + +def b(*args, **kwargs): print(banner (*args, **kwargs)) +def d(*args, **kwargs): print(qmd.div(*args, **kwargs)) + +### +# Output section +### + +b(f"""# Data Analysis using
Bootstrap\-Coupled ESTimation + +### Analyze your data with effect sizes and beautiful estimation plots. + +{btn('Get started', '/01-getting_started.ipynb')} + +{img('showpiece.png', style={"margin-top": "20px", "margin-bottom": "20px"}, link=True)}""", "content-block") + +feature_h = banner(f"""## Robust and Beautiful
Statistical Visualization + +### Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values.""") + +d(feature_h+feature_d, "content-block") + +b(f"""## Get started in seconds + +{btn('Install dabest', '/01-getting_started.ipynb')}""", 'content-block', style={"margin-top": "40px"}) + + + + + + diff --git a/nbs/iris.png b/nbs/iris.png new file mode 100644 index 00000000..92d58275 Binary files /dev/null and b/nbs/iris.png differ diff --git a/nbs/nbdev.yml b/nbs/nbdev.yml new file mode 100644 index 00000000..d5ab7123 --- /dev/null +++ b/nbs/nbdev.yml @@ -0,0 +1,9 @@ +project: + output-dir: _docs + +website: + title: "dabest" + site-url: "https://ZHANGROU-99.github.io/DABEST-python" + description: "Data Analysis and Visualization using Bootstrap-Coupled Estimation." + repo-branch: master + repo-url: "https://github.com/ZHANGROU-99/DABEST-python" diff --git a/dabest/pytest.ini b/nbs/pytest.ini similarity index 72% rename from dabest/pytest.ini rename to nbs/pytest.ini index 9ed7c623..60beac8c 100644 --- a/dabest/pytest.ini +++ b/nbs/pytest.ini @@ -3,7 +3,7 @@ filterwarnings = ignore::UserWarning ignore::DeprecationWarning -addopts = --mpl --mpl-baseline-path=dabest/tests/baseline_images +addopts = --mpl --mpl-baseline-path=nbs/tests/baseline_images markers = mpl_image_compare: mark a test as implementing mpl image comparison. \ No newline at end of file diff --git a/nbs/read_me.ipynb b/nbs/read_me.ipynb new file mode 100644 index 00000000..c89729cc --- /dev/null +++ b/nbs/read_me.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "205a828a", + "metadata": {}, + "source": [ + "# DABEST-Python\n", + "\n", + "[![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.6-6666ff.svg)](https://www.anaconda.com/distribution/)\n", + "[![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)\n", + "[![Downloads](https://pepy.tech/badge/dabest/month)](https://pepy.tech/project/dabest/month)\n", + "[![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4)\n", + "[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)" + ] + }, + { + "cell_type": "markdown", + "id": "8fcb9b6e", + "metadata": {}, + "source": [ + "## Recent Version Update\n", + "\n", + "On 20 March 2023, we officially released **DABEST v2023.02.14 for Python**. This new version provided the following new features:\n", + "\n", + "1. **Repeated measures.** Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis.\n", + "\n", + "2. **Proportional data.** Generates proportional bar plots, proportional differences, and calculates Cohen's h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fischer's exact test.\n", + "\n", + "3. **The $\\Delta\\Delta$ plot.** Calculates the delta-delta ($\\Delta\\Delta$) for 2 × 2 experimental designs and plots the four groups with their relevant effect sizes. This design can be used as a replacement for the 2 × 2 ANOVA.\n", + "\n", + "4. **Mini-meta.** Calculates and plots a weighted delta ($\\Delta$) for meta-analysis of experimental replicates. Useful for summarizing data from multiple replicated experiments, for example by different scientists in the same lab, or the same scientist at different times. When the observed values are known (and share a common metric), this makes meta-analysis available as a routinely accessible tool." + ] + }, + { + "cell_type": "markdown", + "id": "ac5ef3e3", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "- [About](#about)\n", + "- [Installation](#installation)\n", + "- [Usage](#usage)\n", + "- [How to cite](#how-to-cite)\n", + "- [Bugs](#bugs)\n", + "- [Contributing](#contributing)\n", + "- [Acknowledgements](#acknowledgements)\n", + "- [Testing](#testing)\n", + "- [DABEST in other languages](#dabest-in-other-languages)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "be09ee89", + "metadata": {}, + "source": [ + "## About\n", + "\n", + "DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.\n", + "\n", + "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by *P* values.\n", + "\n", + "An estimation plot has two key features.\n", + "\n", + "1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.\n", + "\n", + "2. It presents the effect size as a **bootstrap 95% confidence interval** on a **separate but aligned axes**.\n", + "\n", + "![The five kinds of estimation plots](showpiece.png \"The five kinds of estimation plots.\")\n", + "\n", + "DABEST powers [estimationstats.com](https://www.estimationstats.com/), allowing everyone access to high-quality estimation plots.\n" + ] + }, + { + "cell_type": "markdown", + "id": "1044e2c0", + "metadata": {}, + "source": [ + "## Installation\n", + "\n", + "This package is tested on Python 3.6, 3.7, and 3.8.\n", + "It is highly recommended to download the [Anaconda distribution](https://www.continuum.io/downloads) of Python in order to obtain the dependencies easily.\n", + "\n", + "You can install this package via `pip`.\n", + "\n", + "To install, at the command line run\n", + "\n", + "```shell\n", + "pip install --upgrade dabest\n", + "```\n", + "You can also [clone](https://help.github.com/articles/cloning-a-repository) this repo locally.\n", + "\n", + "Then, navigate to the cloned repo in the command line and run\n", + "\n", + "```shell\n", + "pip install .\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "bf4d69d6", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "```python3\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "# Load the iris dataset. Requires internet access.\n", + "iris = pd.read_csv(\"https://github.com/mwaskom/seaborn-data/raw/master/iris.csv\")\n", + "\n", + "# Load the above data into `dabest`.\n", + "iris_dabest = dabest.load(data=iris, x=\"species\", y=\"petal_width\",\n", + " idx=(\"setosa\", \"versicolor\", \"virginica\"))\n", + "\n", + "# Produce a Cumming estimation plot.\n", + "iris_dabest.mean_diff.plot();\n", + "```\n", + "![A Cumming estimation plot of petal width from the iris dataset](iris.png)\n", + "\n", + "Please refer to the official [tutorial](https://acclab.github.io/DABEST-python-docs/tutorial.html) for more useful code snippets.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "af18a2c0", + "metadata": {}, + "source": [ + "## How to cite\n", + "\n", + "**Moving beyond P values: Everyday data analysis with estimation plots**\n", + "\n", + "*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang*\n", + "\n", + "Nature Methods 2019, 1548-7105. [10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3)\n", + "\n", + "[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view PDF](https://rdcu.be/bHhJ4)\n", + "\n", + "\n", + "## Bugs\n", + "\n", + "Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/DABEST-python/issues/new).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "24b19856", + "metadata": {}, + "source": [ + "## Contributing\n", + "\n", + "All contributions are welcome; please read the [Guidelines for contributing](https://github.com/ACCLAB/DABEST-python/blob/master/CONTRIBUTING.md) first.\n", + "\n", + "We also have a [Code of Conduct](https://github.com/ACCLAB/DABEST-python/blob/master/CODE_OF_CONDUCT.md) to foster an inclusive and productive space.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b1e2578a", + "metadata": {}, + "source": [ + "### A wish list for new features\n", + "Currently, DABEST offers functions to handle data traditionally analyzed with Student's paired and unpaired t-tests. It also offers plots for multiplexed versions of these, and the estimation counterpart to a 1-way analysis of variance (ANOVA), the shared-control design. While these five functions execute a large fraction of common biomedical data analyses, there remain three others: 2-way data, time-series group data, and proportional data. We aim to add these new functions to both the R and Python libraries.\n", + "\n", + "- In many experiments, four groups are investigate to isolate an interaction, for example: a genotype × drug effect. Here, wild-type and mutant animals are each subjected to drug or sham treatments; the data are traditionally analysed with a 2×2 ANOVA. We have received requests by email, Twitter, and GitHub to implement an estimation counterpart to the 2-way ANOVA. To do this, we will implement $\\Delta\\Delta$ plots, in which the difference of means ($\\Delta$) of two groups is subtracted from a second two-group $\\Delta$. **Implemented in v2023.02.14.**\n", + "\n", + "- Currently, DABEST can analyse multiple paired data in a single plot, and multiple groups with a common, shared control. However, a common design in biomedical science is to follow the same group of subjects over multiple, successive time points. An estimation plot for this would combine elements of the two other designs, and could be used in place of a repeated-measures ANOVA. **Implemented in v2023.02.14**\n", + "\n", + "- We have observed that proportional data are often analyzed in neuroscience and other areas of biomedical research. However, compared to other data types, the charts are frequently impoverished: often, they omit error bars, sample sizes, and even P values—let alone effect sizes. We would like DABEST to feature proportion charts, with error bars and a curve for the distribution of the proportional differences. **Implemented in v2023.02.14**\n", + "\n", + "We encourage contributions for the above features. " + ] + }, + { + "cell_type": "markdown", + "id": "a528d95d", + "metadata": {}, + "source": [ + "## Acknowledgements\n", + "\n", + "We would like to thank alpha testers from the [Claridge-Chang lab](https://www.claridgechang.net/): [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and Stanislav Ott.\n", + "\n", + "\n", + "## Testing\n", + "\n", + "To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest).\n", + "\n", + "Run `pytest` in the root directory of the source distribution. This runs the test suite in the folder `dabest/tests`. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.\n", + "\n", + "\n", + "## DABEST in other languages\n", + "\n", + "DABEST is also available in R ([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab ([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2b56748", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/showpiece.png b/nbs/showpiece.png new file mode 100644 index 00000000..6e46cd50 Binary files /dev/null and b/nbs/showpiece.png differ diff --git a/nbs/styles.css b/nbs/styles.css new file mode 100644 index 00000000..66ccc49e --- /dev/null +++ b/nbs/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; 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a/nbs/tests/test_01_effsizes_pvals.ipynb b/nbs/tests/test_01_effsizes_pvals.ipynb new file mode 100644 index 00000000..fa848f90 --- /dev/null +++ b/nbs/tests/test_01_effsizes_pvals.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "6e2c4075", + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "import lqrt\n", + "import numpy as np\n", + "from numpy import median as npmedian\n", + "from numpy import mean as npmean\n", + "import scipy as sp\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f9abde6", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._stats_tools import effsize\n", + "from dabest._classes import TwoGroupsEffectSize, PermutationTest, Dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30de06ac", + "metadata": {}, + "outputs": [], + "source": [ + "# Data for tests.\n", + "# See Cumming, G. Understanding the New Statistics:\n", + "# Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, 2012,\n", + "# from Cumming 2012 Table 11.1 Pg 287.\n", + "wb = {\"control\": [34, 54, 33, 44, 45, 53, 37, 26, 38, 58],\n", + " \"expt\": [66, 38, 35, 55, 48, 39, 65, 32, 57, 41]}\n", + "wellbeing = pd.DataFrame(wb)\n", + "\n", + "\n", + "\n", + "# from Cumming 2012 Table 11.2 Page 291\n", + "paired_wb = {\"pre\": [43, 28, 54, 36, 31, 48, 50, 69, 29, 40],\n", + " \"post\": [51, 33, 58, 42, 39, 45, 54, 68, 35, 44],\n", + " \"ID\": np.arange(10)}\n", + "paired_wellbeing = pd.DataFrame(paired_wb)\n", + "\n", + "\n", + "\n", + "# Data for testing Cohen's calculation.\n", + "# Only work with binary data.\n", + "# See Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.\n", + "# Make two groups of `smoke` by choosing `low` as a standard, and the data is trimed from the back.\n", + "sk = { \"low\": [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, \n", + " 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0],\n", + " \"high\": [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, \n", + " 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1]}\n", + "smoke = pd.DataFrame(sk)\n", + "\n", + "\n", + "\n", + "# Data from Hogarty and Kromrey (1999)\n", + "# Kromrey, Jeffrey D., and Kristine Y. Hogarty. 1998.\n", + "# \"Analysis Options for Testing Group Differences on Ordered Categorical\n", + "# Variables: An Empirical Investigation of Type I Error Control\n", + "# Statistical Power.\"\n", + "# Multiple Linear Regression Viewpoints 25 (1): 70 - 82.\n", + "likert_control = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5]\n", + "likert_treatment = [1, 2, 3, 4, 4, 5]\n", + "\n", + "\n", + "\n", + "# Data from Cliff (1993)\n", + "# Cliff, Norman. 1993. \"Dominance Statistics: Ordinal Analyses to Answer\n", + "# Ordinal Questions.\"\n", + "# Psychological Bulletin 114 (3): 494-509.\n", + "a_scores = [6, 7, 9, 10]\n", + "b_scores = [1, 3, 4, 7, 8]\n", + "\n", + "\n", + "\n", + "# kwargs for Dabest class init.\n", + "dabest_default_kwargs = dict(x=None, y=None, ci=95, \n", + " resamples=5000, random_seed=12345,\n", + " proportional=False, delta2=False, experiment=None, \n", + " experiment_label=None, x1_level=None, mini_meta=False\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "8443abc1", + "metadata": {}, + "source": [ + "test_mean_diff_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f61f756", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff = effsize.func_difference(wellbeing.control, wellbeing.expt,\n", + " np.mean, is_paired=False)\n", + "assert mean_diff == pytest.approx(5.4)" + ] + }, + { + "cell_type": "markdown", + "id": "ed34f114", + "metadata": {}, + "source": [ + "test_median_diff_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "767dd8cf", + "metadata": {}, + "outputs": [], + "source": [ + "median_diff = effsize.func_difference(wellbeing.control, wellbeing.expt,\n", + " npmedian, is_paired=False)\n", + "assert median_diff == pytest.approx(3.5)" + ] + }, + { + "cell_type": "markdown", + "id": "3b5a8f2f", + "metadata": {}, + "source": [ + "test_mean_diff_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0392c89c", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff = effsize.func_difference(paired_wellbeing.pre,\n", + " paired_wellbeing.post,\n", + " npmean, is_paired=\"baseline\")\n", + "assert mean_diff == pytest.approx(4.10)" + ] + }, + { + "cell_type": "markdown", + "id": "2e8c9402", + "metadata": {}, + "source": [ + "test_median_diff_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dff5a08", + "metadata": {}, + "outputs": [], + "source": [ + "median_diff = effsize.func_difference(paired_wellbeing.pre,\n", + " paired_wellbeing.post,\n", + " npmedian, is_paired=\"baseline\")\n", + "assert median_diff == pytest.approx(4.5)" + ] + }, + { + "cell_type": "markdown", + "id": "246036ea", + "metadata": {}, + "source": [ + "test_cohens_d_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c4f2889", + "metadata": {}, + "outputs": [], + "source": [ + "cohens_d = effsize.cohens_d(wellbeing.control, wellbeing.expt,\n", + " is_paired=False)\n", + "assert np.round(cohens_d, 2) == pytest.approx(0.47)" + ] + }, + { + "cell_type": "markdown", + "id": "dfc37dbc", + "metadata": {}, + "source": [ + "test_hedges_g_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47d33c67", + "metadata": {}, + "outputs": [], + "source": [ + "hedges_g = effsize.hedges_g(wellbeing.control, wellbeing.expt,\n", + " is_paired=False)\n", + "assert np.round(hedges_g, 2) == pytest.approx(0.45)" + ] + }, + { + "cell_type": "markdown", + "id": "4fd38d44", + "metadata": {}, + "source": [ + "test_cohens_d_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec76bc0f", + "metadata": {}, + "outputs": [], + "source": [ + "cohens_d = effsize.cohens_d(paired_wellbeing.pre, paired_wellbeing.post,\n", + " is_paired=\"baseline\")\n", + "assert np.round(cohens_d, 2) == pytest.approx(0.34)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2a772b30", + "metadata": {}, + "source": [ + "test_hedges_g_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe28bdc1", + "metadata": {}, + "outputs": [], + "source": [ + "hedges_g = effsize.hedges_g(paired_wellbeing.pre, paired_wellbeing.post,\n", + " is_paired=\"baseline\")\n", + "assert np.round(hedges_g, 2) == pytest.approx(0.33)" + ] + }, + { + "cell_type": "markdown", + "id": "c70d5415", + "metadata": {}, + "source": [ + "test_cohens_h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36ddc28c", + "metadata": {}, + "outputs": [], + "source": [ + "cohens_h = effsize.cohens_h(smoke.low, smoke.high)\n", + "assert np.round(cohens_h, 2) == pytest.approx(0.17)" + ] + }, + { + "cell_type": "markdown", + "id": "85935481", + "metadata": {}, + "source": [ + "test_cliffs_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33bd09cc", + "metadata": {}, + "outputs": [], + "source": [ + "likert_delta = effsize.cliffs_delta(likert_treatment, likert_control)\n", + "assert likert_delta == pytest.approx(-0.25)\n", + "\n", + "scores_delta = effsize.cliffs_delta(b_scores, a_scores)\n", + "assert scores_delta == pytest.approx(0.65)" + ] + }, + { + "cell_type": "markdown", + "id": "ca1a50c5", + "metadata": {}, + "source": [ + "test_unpaired_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16884a64", + "metadata": {}, + "outputs": [], + "source": [ + "c = wellbeing.control\n", + "t = wellbeing.expt\n", + "\n", + "unpaired_es = TwoGroupsEffectSize(c, t, \"mean_diff\", is_paired=False, proportional=False)\n", + "\n", + "p1 = sp.stats.mannwhitneyu(c, t, alternative=\"two-sided\").pvalue\n", + "assert unpaired_es.pvalue_mann_whitney == pytest.approx(p1)\n", + "\n", + "p2 = sp.stats.ttest_ind(c, t, nan_policy='omit').pvalue\n", + "assert unpaired_es.pvalue_students_t == pytest.approx(p2)\n", + "\n", + "p3 = sp.stats.ttest_ind(c, t, equal_var=False, nan_policy='omit').pvalue\n", + "assert unpaired_es.pvalue_welch == pytest.approx(p3)" + ] + }, + { + "cell_type": "markdown", + "id": "0ced5798", + "metadata": {}, + "source": [ + "test_paired_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5be74408", + "metadata": {}, + "outputs": [], + "source": [ + "before = paired_wellbeing.pre\n", + "after = paired_wellbeing.post\n", + "\n", + "paired_es = TwoGroupsEffectSize(before, after, \"mean_diff\", is_paired=\"baseline\", proportional=False)\n", + "\n", + "p1 = sp.stats.ttest_rel(before, after, nan_policy='omit').pvalue\n", + "assert paired_es.pvalue_paired_students_t == pytest.approx(p1)\n", + "\n", + "p2 = sp.stats.wilcoxon(before, after).pvalue\n", + "assert paired_es.pvalue_wilcoxon == pytest.approx(p2)" + ] + }, + { + "cell_type": "markdown", + "id": "10b9fb80", + "metadata": {}, + "source": [ + "test_median_diff_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "371d7182", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhang\\Desktop\\vnbdev-dabest\\DABEST-python\\dabest\\effsize.py:77: UserWarning: Using median as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using a different statistic, such as the mean.\n", + "When plotting, please consider using percetile confidence intervals by specifying `ci_type='percentile'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n", + "\n", + " warnings.warn(message=mes1+mes2, category=UserWarning)\n" + ] + } + ], + "source": [ + "c = wellbeing.control\n", + "t = wellbeing.expt\n", + "\n", + "es = TwoGroupsEffectSize(c, t, \"median_diff\", is_paired=False, proportional=False)\n", + "\n", + "p1 = sp.stats.kruskal(c, t, nan_policy='omit').pvalue\n", + "assert es.pvalue_kruskal == pytest.approx(p1)" + ] + }, + { + "cell_type": "markdown", + "id": "fbf5962b", + "metadata": {}, + "source": [ + "test_ordinal_dominance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65a0d9c6", + "metadata": {}, + "outputs": [], + "source": [ + "es = TwoGroupsEffectSize(likert_control, likert_treatment, \n", + " \"cliffs_delta\", is_paired=False, proportional=False)\n", + " \n", + "p1 = sp.stats.brunnermunzel(likert_control, likert_treatment).pvalue\n", + "assert es.pvalue_brunner_munzel == pytest.approx(p1)" + ] + }, + { + "cell_type": "markdown", + "id": "34885930", + "metadata": {}, + "source": [ + "test_unpaired_permutation_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44d1b0d3", + "metadata": {}, + "outputs": [], + "source": [ + "perm_test = PermutationTest(wellbeing.control, wellbeing.expt, \n", + " effect_size=\"mean_diff\", \n", + " is_paired=False)\n", + "assert perm_test.pvalue == pytest.approx(0.2976) " + ] + }, + { + "cell_type": "markdown", + "id": "c36603ed", + "metadata": {}, + "source": [ + "test_paired_permutation_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45477ae1", + "metadata": {}, + "outputs": [], + "source": [ + "perm_test = PermutationTest(paired_wellbeing.pre, \n", + " paired_wellbeing.post, \n", + " effect_size=\"mean_diff\", \n", + " is_paired=\"baseline\")\n", + "assert perm_test.pvalue == pytest.approx(0.0124)" + ] + }, + { + "cell_type": "markdown", + "id": "3279e7c7", + "metadata": {}, + "source": [ + "test_lqrt_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78a98593", + "metadata": {}, + "outputs": [], + "source": [ + "unpaired_dabest = Dabest(wellbeing, idx=(\"control\", \"expt\"), \n", + " paired=None, id_col=None, \n", + " **dabest_default_kwargs)\n", + "lqrt_result = unpaired_dabest.mean_diff.lqrt\n", + "\n", + "p1 = lqrt.lqrtest_ind(wellbeing.control, wellbeing.expt,\n", + " equal_var=True,\n", + " random_state=12345)\n", + "\n", + "p2 = lqrt.lqrtest_ind(wellbeing.control, wellbeing.expt,\n", + " equal_var=False,\n", + " random_state=12345)\n", + "\n", + "assert lqrt_result.pvalue_lqrt_equal_var[0] == pytest.approx(p1.pvalue)\n", + "assert lqrt_result.pvalue_lqrt_unequal_var[0] == pytest.approx(p2.pvalue)" + ] + }, + { + "cell_type": "markdown", + "id": "fd63ddff", + "metadata": {}, + "source": [ + "test_lqrt_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "680aa3f0", + "metadata": {}, + "outputs": [], + "source": [ + "paired_dabest = Dabest(paired_wellbeing, idx=(\"pre\", \"post\"),\n", + " paired=\"baseline\", id_col=\"ID\",\n", + " **dabest_default_kwargs)\n", + "lqrt_result = paired_dabest.mean_diff.lqrt\n", + "\n", + "p1 = lqrt.lqrtest_rel(paired_wellbeing.pre, paired_wellbeing.post, \n", + " random_state=12345)\n", + "\n", + "assert lqrt_result.pvalue_paired_lqrt[0] == pytest.approx(p1.pvalue)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tests/test_02_edge_cases.ipynb b/nbs/tests/test_02_edge_cases.ipynb new file mode 100644 index 00000000..27821eee --- /dev/null +++ b/nbs/tests/test_02_edge_cases.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a25d3bb6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from numpy.random import PCG64, RandomState\n", + "import scipy as sp\n", + "import pytest\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "359558a0", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._api import load" + ] + }, + { + "cell_type": "markdown", + "id": "f538f98b", + "metadata": {}, + "source": [ + "### test_unrelated_columns\n", + "\n", + " Test to see if 'unrelated' columns jam up the analysis.\n", + " See Github Issue 43.\n", + " https://github.com/ACCLAB/DABEST-python/issues/44.\n", + " \n", + " Added in v0.2.5.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "417bd33a", + "metadata": {}, + "outputs": [], + "source": [ + "N=60\n", + "random_seed=12345\n", + "\n", + "# rng = RandomState(MT19937(random_seed))\n", + "rng = RandomState(PCG64(12345))\n", + "# rng = np.random.default_rng(seed=random_seed)\n", + "\n", + "df = pd.DataFrame(\n", + " {'groups': rng.choice(['Group 1', 'Group 2', 'Group 3'], size=(N,)),\n", + " 'color' : rng.choice(['green', 'red', 'purple'], size=(N,)),\n", + " 'value': rng.random(size=(N,))})\n", + "\n", + "df['unrelated'] = np.nan\n", + "\n", + "test = load(data=df, x='groups', y='value', \n", + " idx=['Group 1', 'Group 2'])\n", + "\n", + "md = test.mean_diff.results\n", + "\n", + "assert md.difference[0] == pytest.approx(-0.0322, abs=1e-4)\n", + "assert md.bca_low[0] == pytest.approx(-0.2279, abs=1e-4)\n", + "assert md.bca_high[0] == pytest.approx(0.1613, abs=1e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afc96b46", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dabest/tests/test_03_plotting.py b/nbs/tests/test_03_plotting.py similarity index 79% rename from dabest/tests/test_03_plotting.py rename to nbs/tests/test_03_plotting.py index 78511321..40a753a9 100644 --- a/dabest/tests/test_03_plotting.py +++ b/nbs/tests/test_03_plotting.py @@ -1,31 +1,60 @@ -#!/usr/bin/env python3 - -# -*- coding: utf-8 -*- - - import pytest import numpy as np -import pandas as pd from scipy.stats import norm - - +import pandas as pd import matplotlib as mpl mpl.use('Agg') -import matplotlib.pyplot as plt import matplotlib.ticker as Ticker +import matplotlib.pyplot as plt + +from dabest._api import load + + +def create_demo_dataset(seed=9999, N=20): + + import numpy as np + import pandas as pd + from scipy.stats import norm # Used in generation of populations. + + np.random.seed(9999) # Fix the seed so the results are replicable. + # pop_size = 10000 # Size of each population. + + # Create samples + c1 = norm.rvs(loc=3, scale=0.4, size=N) + c2 = norm.rvs(loc=3.5, scale=0.75, size=N) + c3 = norm.rvs(loc=3.25, scale=0.4, size=N) + t1 = norm.rvs(loc=3.5, scale=0.5, size=N) + t2 = norm.rvs(loc=2.5, scale=0.6, size=N) + t3 = norm.rvs(loc=3, scale=0.75, size=N) + t4 = norm.rvs(loc=3.5, scale=0.75, size=N) + t5 = norm.rvs(loc=3.25, scale=0.4, size=N) + t6 = norm.rvs(loc=3.25, scale=0.4, size=N) -from .._api import load -from .utils import create_demo_dataset + # Add a `gender` column for coloring the data. + females = np.repeat('Female', N/2).tolist() + males = np.repeat('Male', N/2).tolist() + gender = females + males + # Add an `id` column for paired data plotting. + id_col = pd.Series(range(1, N+1)) + # Combine samples and gender into a DataFrame. + df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, + 'Control 2' : c2, 'Test 2' : t2, + 'Control 3' : c3, 'Test 3' : t3, + 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6, + 'Gender' : gender, 'ID' : id_col + }) + + return df df = create_demo_dataset() two_groups_unpaired = load(df, idx=("Control 1", "Test 1")) two_groups_paired = load(df, idx=("Control 1", "Test 1"), - paired=True, id_col="ID") + paired="baseline", id_col="ID") multi_2group = load(df, idx=(("Control 1", "Test 1",), ("Control 2", "Test 2")) @@ -34,7 +63,7 @@ multi_2group_paired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), - paired=True, id_col="ID") + paired="baseline", id_col="ID") shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", @@ -47,6 +76,18 @@ ) ) +multi_groups_baseline = load(df, idx=(("Control 1", "Test 1",), + ("Control 2", "Test 2","Test 3"), + ("Control 3", "Test 4","Test 5", "Test 6") + ), paired="baseline", id_col="ID" + ) + +multi_groups_sequential = load(df, idx=(("Control 1", "Test 1",), + ("Control 2", "Test 2","Test 3"), + ("Control 3", "Test 4","Test 5", "Test 6") + ), paired="sequential", id_col="ID" + ) + @pytest.mark.mpl_image_compare(tolerance=10) @@ -137,7 +178,7 @@ def test_11_inset_plots(): iris_dabest3 = load(data=iris_melt[iris_melt.species=="setosa"], x="metric", y="value", idx=("sepal_length", "sepal_width"), - paired=True, id_col="index") + paired="baseline", id_col="index") @@ -340,7 +381,7 @@ def test_28_unpaired_cumming_reflines_kwargs(): @pytest.mark.mpl_image_compare(tolerance=10) -def test_28_paired_cumming_slopegraph_reflines_kwargs(): +def test_29_paired_cumming_slopegraph_reflines_kwargs(): return two_groups_paired.mean_diff.plot(float_contrast=False, color_col="Gender", @@ -350,10 +391,18 @@ def test_28_paired_cumming_slopegraph_reflines_kwargs(): contrast_ylim=(-1, 1) ); +@pytest.mark.mpl_image_compare(tolerance=10) +def test_30_sequential_cumming_slopegraph(): + return multi_groups_sequential.mean_diff.plot(); + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_31_baseline_cumming_slopegraph(): + return multi_groups_baseline.mean_diff.plot(); + @pytest.mark.mpl_image_compare(tolerance=10) def test_99_style_sheets(): # Perform this test last so we don't have to reset the plot style. plt.style.use("dark_background") - return multi_2group.mean_diff.plot(); + return multi_2group.mean_diff.plot(); \ No newline at end of file diff --git a/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb b/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb new file mode 100644 index 00000000..b3f77f83 --- /dev/null +++ b/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e7a36df7", + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "import lqrt\n", + "import numpy as np\n", + "import scipy as sp\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7094f95", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._stats_tools import effsize\n", + "from dabest._classes import TwoGroupsEffectSize, PermutationTest, Dabest, EffectSizeDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d83b66c0", + "metadata": {}, + "outputs": [], + "source": [ + "# Data for tests\n", + "# See Der, G., & Everitt, B. S. (2009). A handbook\n", + "# of statistical analyses using SAS, from Display 11.1\n", + "group = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n", + "first = [20, 14, 7, 6, 9, 9, 7, 18, 6, 10, 5, 11, 10, 17, 16, 7, 5, 16, 2, 7, 9, 2, 7, 19,\n", + " 7, 9, 6, 13, 9, 6, 11, 7, 8, 3, 4, 11, 1, 6, 0, 18, 15, 10, 6, 9, 4, 4, 10]\n", + "second = [15, 12, 5, 10, 7, 9, 3, 17, 9, 15, 9, 11, 2, 12, 15, 10, 0, 7, 1, 11, 16,\n", + " 5, 3, 13, 5, 12, 7, 18, 10, 7, 11, 10, 18, 3, 10, 10, 3, 7, 3, 18, 15, 14, 6, 9, 3, 13, 11]\n", + "third = [14, 12, 5, 9, 9, 9, 7, 16, 9, 12, 7, 8, 9, 14, 12, 4, 5, 7, 1, 7, 14, 6, 5, 14, 8, 16, 10,\n", + " 14, 12, 8, 12, 11, 19, 3, 11, 10, 2, 7, 3, 19, 15, 16, 7, 13, 4, 13, 13]\n", + "fourth = [13, 10, 6, 8, 5, 11, 6, 14, 9, 12, 3, 8, 3, 10, 7, 7, 0, 6, 2, 5, 10, 7, 5, 12, 8, 17, 15,\n", + " 21, 14, 9, 14, 12, 19, 7, 17, 15, 4, 9, 4, 22, 18, 17, 9, 16, 7, 16, 17]\n", + "fifth = [13, 10, 5, 7, 4, 8, 5, 12, 9, 11, 5, 9, 5, 9, 9, 5, 0, 4, 2, 8, 6, 6, 5, 10, 6, 18, 16, 21,\n", + " 15, 12, 16, 14, 22, 8, 18, 16, 5, 10, 6, 22, 19, 19, 10, 20, 9, 19, 21] \n", + "\n", + "df = pd.DataFrame({'Group' : group,\n", + " 'First' : first,\n", + " 'Second': second,\n", + " 'Third' : third,\n", + " 'Fourth': fourth,\n", + " 'Fifth' : fifth,\n", + " 'ID': np.arange(0, 47)\n", + " })\n", + "\n", + "# kwargs for Dabest class init.\n", + "dabest_default_kwargs = dict(x=None, y=None, ci=95, \n", + " resamples=5000, random_seed=12345, proportional=False,\n", + " delta2 = False, experiment=None, \n", + " experiment_label=None, x1_level=None, mini_meta=False)\n", + "\n", + "# example of sequential repeated measures\n", + "sequential = Dabest(df, id_col = \"ID\",\n", + " idx=(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\"),\n", + " paired = \"sequential\",\n", + " **dabest_default_kwargs)\n", + "\n", + "# example of baseline repeated measures\n", + "baseline = Dabest(df, id_col = \"ID\",\n", + " idx=(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\"),\n", + " paired = \"baseline\",\n", + " **dabest_default_kwargs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "476b71b7", + "metadata": {}, + "source": [ + "test_mean_diff_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c771e929", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff = sequential.mean_diff.results['difference'].to_list()\n", + "np_result = [np.mean(df.iloc[:,i+1]-df.iloc[:,i]) for i in range(1,5)]\n", + "assert mean_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "4140c277", + "metadata": {}, + "source": [ + "test_median_diff_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65cf8502", + "metadata": {}, + "outputs": [], + "source": [ + "median_diff = sequential.median_diff.results['difference'].to_list()\n", + "np_result = [np.median(df.iloc[:,i+1]-df.iloc[:,i]) for i in range(1,5)]\n", + "assert median_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "97a15450", + "metadata": {}, + "source": [ + "test_mean_diff_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0325ff0b", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff = baseline.mean_diff.results['difference'].to_list()\n", + "np_result = [np.mean(df.iloc[:,i]-df.iloc[:,1]) for i in range(2,6)]\n", + "assert mean_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "483c03ec", + "metadata": {}, + "source": [ + "test_median_diff_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c568cd42", + "metadata": {}, + "outputs": [], + "source": [ + "median_diff = baseline.median_diff.results['difference'].to_list()\n", + "np_result = [np.median(df.iloc[:,i]-df.iloc[:,1]) for i in range(2,6)]\n", + "assert median_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "3095a98c", + "metadata": {}, + "source": [ + "test_cohens_d_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2485a2df", + "metadata": {}, + "outputs": [], + "source": [ + "cohens_d = sequential.cohens_d.results['difference'].to_list()\n", + "np_result = [np.mean(df.iloc[:,i+1]-df.iloc[:,i])\n", + " /np.sqrt((np.var(df.iloc[:,i+1], ddof=1)+np.var(df.iloc[:,i], ddof=1))/2) \n", + " for i in range(1,5)]\n", + "assert cohens_d == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "ba3546ce", + "metadata": {}, + "source": [ + "test_hedges_g_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29b0cc7c", + "metadata": {}, + "outputs": [], + "source": [ + "from math import gamma\n", + "hedges_g = sequential.hedges_g.results['difference'].to_list()\n", + "a = 47*2-2\n", + "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", + "np_result = [np.mean(df.iloc[:,i+1]-df.iloc[:,i])*fac\n", + " /np.sqrt((np.var(df.iloc[:,i+1], ddof=1)+np.var(df.iloc[:,i], ddof=1))/2) \n", + " for i in range(1,5)] \n", + "assert hedges_g == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "c4a96519", + "metadata": {}, + "source": [ + "test_cohens_d_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93d9b97f", + "metadata": {}, + "outputs": [], + "source": [ + "cohens_d = baseline.cohens_d.results['difference'].to_list()\n", + "np_result = [np.mean(df.iloc[:,i]-df.iloc[:,1])\n", + " /np.sqrt((np.var(df.iloc[:,i], ddof=1)+np.var(df.iloc[:,1], ddof=1))/2) \n", + " for i in range(2,6)]\n", + "assert cohens_d == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "c2c71cac", + "metadata": {}, + "source": [ + "test_hedges_g_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b60e360", + "metadata": {}, + "outputs": [], + "source": [ + "from math import gamma\n", + "hedges_g = baseline.hedges_g.results['difference'].to_list()\n", + "a = 47*2-2\n", + "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", + "np_result = [np.mean(df.iloc[:,i]-df.iloc[:,1])*fac\n", + " /np.sqrt((np.var(df.iloc[:,i], ddof=1)+np.var(df.iloc[:,1], ddof=1))/2) \n", + " for i in range(2,6)]\n", + "assert hedges_g == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "eb5d853c", + "metadata": {}, + "source": [ + "test_paired_stats_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9452641e", + "metadata": {}, + "outputs": [], + "source": [ + "np_result = sequential.mean_diff.results\n", + " \n", + "p1 = [sp.stats.ttest_rel(df.iloc[:,i], df.iloc[:,i+1], nan_policy='omit').pvalue\n", + " for i in range(1,5)] \n", + "assert np_result[\"pvalue_paired_students_t\"].to_list() == pytest.approx(p1)\n", + "\n", + "p2 = [sp.stats.wilcoxon(df.iloc[:,i], df.iloc[:,i+1]).pvalue\n", + " for i in range(1,5)] \n", + "assert np_result[\"pvalue_wilcoxon\"].to_list() == pytest.approx(p2)" + ] + }, + { + "cell_type": "markdown", + "id": "ca3cf91e", + "metadata": {}, + "source": [ + "test_paired_stats_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "412aa272", + "metadata": {}, + "outputs": [], + "source": [ + "np_result = baseline.mean_diff.results\n", + " \n", + "p1 = [sp.stats.ttest_rel(df.iloc[:,1], df.iloc[:,i], nan_policy='omit').pvalue\n", + " for i in range(2,6)] \n", + "assert np_result[\"pvalue_paired_students_t\"].to_list() == pytest.approx(p1)\n", + "\n", + "p2 = [sp.stats.wilcoxon(df.iloc[:,1], df.iloc[:,i]).pvalue\n", + " for i in range(2,6)] \n", + "assert np_result[\"pvalue_wilcoxon\"].to_list() == pytest.approx(p2)" + ] + }, + { + "cell_type": "markdown", + "id": "b7ba349a", + "metadata": {}, + "source": [ + "test_lqrt_paired_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e20ecd3", + "metadata": {}, + "outputs": [], + "source": [ + "lqrt_result = sequential.mean_diff.lqrt[\"pvalue_paired_lqrt\"].to_list()\n", + " \n", + "p1 = [lqrt.lqrtest_rel(df.iloc[:,i], df.iloc[:,i+1], random_state=12345).pvalue\n", + " for i in range(1,5)] \n", + "\n", + "assert lqrt_result == pytest.approx(p1)" + ] + }, + { + "cell_type": "markdown", + "id": "f43ac68d", + "metadata": {}, + "source": [ + "test_lqrt_paired_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5aee61a0", + "metadata": {}, + "outputs": [], + "source": [ + "lqrt_result = baseline.mean_diff.lqrt[\"pvalue_paired_lqrt\"].to_list()\n", + " \n", + "p1 = [lqrt.lqrtest_rel(df.iloc[:,1], df.iloc[:,i], random_state=12345).pvalue\n", + " for i in range(2,6)] \n", + "\n", + "assert lqrt_result == pytest.approx(p1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb b/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb new file mode 100644 index 00000000..521117dc --- /dev/null +++ b/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb @@ -0,0 +1,613 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "4cfc6826", + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "import lqrt\n", + "import numpy as np\n", + "import scipy as sp\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "381bae02", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._stats_tools import effsize\n", + "from dabest._classes import TwoGroupsEffectSize, PermutationTest, Dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77080b66", + "metadata": {}, + "outputs": [], + "source": [ + "# Data for tests.\n", + "# See: Asheber Abebe. Introduction to Design and Analysis of Experiments \n", + "# with the SAS, from Example: Two-way RM Design Pg 137.\n", + "hr = [72, 78, 71, 72, 66, 74, 62, 69, 69, 66, 84, 80, 72, 65, 75, 71, \n", + " 86, 83, 82, 83, 79, 83, 73, 75, 73, 62, 90, 81, 72, 62, 69, 70]\n", + "\n", + "# Add experiment column\n", + "e1 = np.repeat('Treatment1', 8).tolist()\n", + "e2 = np.repeat('Control', 8).tolist()\n", + "experiment = e1 + e2 + e1 + e2\n", + "\n", + "# Add a `Drug` column as the first variable\n", + "d1 = np.repeat('AX23', 8).tolist()\n", + "d2 = np.repeat('CONTROL', 8).tolist()\n", + "drug = d1 + d2 + d1 + d2\n", + "\n", + "# Add a `Time` column as the second variable\n", + "t1 = np.repeat('T1', 16).tolist()\n", + "t2 = np.repeat('T2', 16).tolist()\n", + "time = t1 + t2\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = []\n", + "for i in range(1, 9):\n", + " id_col.append(str(i)+\"a\")\n", + "for i in range(1, 9):\n", + " id_col.append(str(i)+\"c\")\n", + "id_col.extend(id_col)\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df_test = pd.DataFrame({'ID' : id_col,\n", + " 'Drug' : drug,\n", + " 'Time' : time, \n", + " 'Experiment': experiment,\n", + " 'Heart Rate': hr\n", + " })\n", + "\n", + "\n", + "df_test_control = df_test[df_test[\"Experiment\"]==\"Control\"]\n", + "df_test_control = df_test_control.pivot(index=\"ID\", columns=\"Time\", values=\"Heart Rate\")\n", + "\n", + "\n", + "df_test_treatment1 = df_test[df_test[\"Experiment\"]==\"Treatment1\"]\n", + "df_test_treatment1 = df_test_treatment1.pivot(index=\"ID\", columns=\"Time\", values=\"Heart Rate\")\n", + "\n", + "\n", + "# kwargs for Dabest class init.\n", + "dabest_default_kwargs = dict(ci=95, \n", + " resamples=5000, random_seed=12345,\n", + " idx=None, proportional=False, mini_meta=False\n", + " )\n", + "\n", + "# example of unpaired delta-delta calculation\n", + "unpaired = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", + " delta2 = True, experiment = \"Experiment\",\n", + " experiment_label=None, x1_level=None, paired=None, id_col=None,\n", + " **dabest_default_kwargs)\n", + "\n", + "\n", + "# example of paired delta-delta calculation\n", + "paired = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", + " delta2 = True, experiment = \"Experiment\", paired=\"sequential\", id_col=\"ID\",\n", + " experiment_label=None, x1_level=None,\n", + " **dabest_default_kwargs)\n", + "\n", + "\n", + "# example of paired data with specified experiment/x1 level\n", + "paired_specified_level = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", + " delta2 = True, experiment = \"Experiment\", paired=\"sequential\", id_col=\"ID\",\n", + " experiment_label=[\"Control\", \"Treatment1\"], x1_level=[\"T2\", \"T1\"],\n", + " **dabest_default_kwargs)" + ] + }, + { + "cell_type": "markdown", + "id": "a7ee54b4", + "metadata": {}, + "source": [ + "test_mean_diff_delta_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c496dd6f", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff_results = unpaired.mean_diff.results\n", + "all_mean_diff = mean_diff_results['difference'].to_list()\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", + "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", + "np_result = [diff1, diff2]\n", + "assert all_mean_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "fb9d5333", + "metadata": {}, + "source": [ + "test_mean_diff_delta_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aff642eb", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff_results = paired.mean_diff.results\n", + "all_mean_diff = mean_diff_results['difference'].to_list()\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", + "diff2 = np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", + "np_result = [diff1, diff2]\n", + "assert all_mean_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "b2e7102f", + "metadata": {}, + "source": [ + "test_mean_diff_delta_paired_specified_level" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d64e6a3e", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff_results = paired_specified_level.mean_diff.results\n", + "all_mean_diff = mean_diff_results['difference'].to_list()\n", + "diff1 = np.mean(df_test_control[\"T1\"]-df_test_control[\"T2\"])\n", + "diff2 = np.mean(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"])\n", + "np_result = [diff1, diff2]\n", + "assert all_mean_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "9b846033", + "metadata": {}, + "source": [ + "test_median_diff_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b7878eb", + "metadata": {}, + "outputs": [], + "source": [ + "all_median_diff = unpaired.median_diff.results\n", + "median_diff = all_median_diff['difference'].to_list()\n", + "diff1 = np.median(df_test_treatment1[\"T2\"])-np.median(df_test_treatment1[\"T1\"])\n", + "diff2 = np.median(df_test_control[\"T2\"])-np.median(df_test_control[\"T1\"])\n", + "np_result = [diff1, diff2]\n", + "assert median_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "2fb4a865", + "metadata": {}, + "source": [ + "test_median_diff_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37c52bfc", + "metadata": {}, + "outputs": [], + "source": [ + "all_median_diff = paired.median_diff.results\n", + "median_diff = all_median_diff['difference'].to_list()\n", + "diff1 = np.median(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", + "diff2 = np.median(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", + "np_result = [diff1, diff2]\n", + "assert median_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "b79f3e7f", + "metadata": {}, + "source": [ + "test_median_diff_paired_specified_level" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66abcc7f", + "metadata": {}, + "outputs": [], + "source": [ + "all_median_diff = paired_specified_level.median_diff.results\n", + "median_diff = all_median_diff['difference'].to_list()\n", + "diff1 = np.median(df_test_control[\"T1\"]-df_test_control[\"T2\"])\n", + "diff2 = np.median(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"])\n", + "np_result = [diff1, diff2]\n", + "assert median_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "5e83804b", + "metadata": {}, + "source": [ + "test_cohens_d_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e202acf4", + "metadata": {}, + "outputs": [], + "source": [ + "all_cohens_d = unpaired.cohens_d.results\n", + "cohens_d = all_cohens_d['difference'].to_list()\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", + "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", + "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", + "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", + "np_result = [diff1, diff2]\t \n", + "assert cohens_d == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "ddeca599", + "metadata": {}, + "source": [ + "test_cohens_d_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71f570b4", + "metadata": {}, + "outputs": [], + "source": [ + "all_cohens_d = paired.cohens_d.results\n", + "cohens_d = all_cohens_d['difference'].to_list()\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", + "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", + "diff2 = np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", + "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", + "np_result = [diff1, diff2]\t \n", + "assert cohens_d == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "1e4daeef", + "metadata": {}, + "source": [ + "test_cohens_d_paired_specified_level" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d32dd490", + "metadata": {}, + "outputs": [], + "source": [ + "all_cohens_d = paired_specified_level.cohens_d.results\n", + "cohens_d = all_cohens_d['difference'].to_list()\n", + "diff1 = np.mean(df_test_control[\"T1\"])-np.mean(df_test_control[\"T2\"])\n", + "diff1 = diff1/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2)\n", + "diff2 = np.mean(df_test_treatment1[\"T1\"])-np.mean(df_test_treatment1[\"T2\"])\n", + "diff2 = diff2/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", + "np_result = [diff1, diff2] \n", + "assert cohens_d == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "5b1f2d2a", + "metadata": {}, + "source": [ + "test_hedges_g_unpaired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6b944ab", + "metadata": {}, + "outputs": [], + "source": [ + "from math import gamma\n", + "hedges_g = unpaired.hedges_g.results['difference'].to_list()\n", + "a = 8*2-2\n", + "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", + "diff1 = (np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"]))*fac\n", + "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", + "diff2 = (np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"]))*fac\n", + "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", + "np_result=[diff1, diff2]\n", + "assert hedges_g == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "e25ad33a", + "metadata": {}, + "source": [ + "test_hedges_g_paired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b2a18ea", + "metadata": {}, + "outputs": [], + "source": [ + "from math import gamma\n", + "hedges_g = paired.hedges_g.results['difference'].to_list()\n", + "a = 8*2-2\n", + "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", + "diff1 = (np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"]))*fac\n", + "diff1 = diff1/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", + "diff2 = (np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"]))*fac\n", + "diff2 = diff2/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", + "np_result=[diff1, diff2]\n", + "assert hedges_g == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "2928958a", + "metadata": {}, + "source": [ + "test_hedges_g_paired_specified_level" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86167882", + "metadata": {}, + "outputs": [], + "source": [ + "from math import gamma\n", + "hedges_g = paired_specified_level.hedges_g.results['difference'].to_list()\n", + "a = 8*2-2\n", + "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", + "diff1 = (np.mean(df_test_control[\"T1\"]-df_test_control[\"T2\"]))*fac\n", + "diff1 = diff1/np.sqrt((np.var(df_test_control[\"T2\"], ddof=1)+np.var(df_test_control[\"T1\"], ddof=1))/2) \n", + "diff2 = (np.mean(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"]))*fac\n", + "diff2 = diff2/np.sqrt((np.var(df_test_treatment1[\"T2\"], ddof=1)+np.var(df_test_treatment1[\"T1\"], ddof=1))/2) \n", + "np_result=[diff1, diff2]\n", + "assert hedges_g == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "42d1366d", + "metadata": {}, + "source": [ + "test_unpaired_delta_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da1dac3d", + "metadata": {}, + "outputs": [], + "source": [ + "delta_delta = unpaired.mean_diff.delta_delta.difference\n", + "\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", + "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", + "np_result = diff2-diff1\n", + "\n", + "assert delta_delta == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "64edc5b7", + "metadata": {}, + "source": [ + "test_paired_delta_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7677ed29", + "metadata": {}, + "outputs": [], + "source": [ + "delta_delta = paired.mean_diff.delta_delta.difference\n", + "\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"] - df_test_treatment1[\"T1\"])\n", + "diff2 = np.mean(df_test_control[\"T2\"] - df_test_control[\"T1\"])\n", + "np_result = diff2-diff1\n", + "\n", + "assert delta_delta == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "33ee19f2", + "metadata": {}, + "source": [ + "test_paired_specified_level_delta_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a413527", + "metadata": {}, + "outputs": [], + "source": [ + "delta_delta = paired_specified_level.mean_diff.delta_delta.difference\n", + "\n", + "diff1 = np.mean(df_test_control[\"T1\"] - df_test_control[\"T2\"])\n", + "diff2 = np.mean(df_test_treatment1[\"T1\"] - df_test_treatment1[\"T2\"])\n", + "np_result = diff2-diff1\n", + "\n", + "assert delta_delta == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "94a0571f", + "metadata": {}, + "source": [ + "test_unpaired_permutation_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f039f9a", + "metadata": {}, + "outputs": [], + "source": [ + "delta_delta = unpaired.mean_diff.delta_delta\n", + "pvalue = delta_delta.pvalue_permutation\n", + "permutations_delta_delta = delta_delta.permutations_delta_delta\n", + "\n", + "perm_test_1 = PermutationTest(df_test_treatment1[\"T1\"], \n", + " df_test_treatment1[\"T2\"], \n", + " effect_size=\"mean_diff\", \n", + " is_paired=False)\n", + "perm_test_2 = PermutationTest(df_test_control[\"T1\"], \n", + " df_test_control[\"T2\"], \n", + " effect_size=\"mean_diff\", \n", + " is_paired=False)\n", + "permutations_1 = perm_test_1.permutations\n", + "permutations_2 = perm_test_2.permutations\n", + "\n", + "delta_deltas = permutations_2-permutations_1\n", + "assert permutations_delta_delta == pytest.approx(delta_deltas)\n", + "\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"])-np.mean(df_test_treatment1[\"T1\"])\n", + "diff2 = np.mean(df_test_control[\"T2\"])-np.mean(df_test_control[\"T1\"])\n", + "np_diff = diff2-diff1\n", + "\n", + "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_diff), \n", + " delta_deltas)))/len(delta_deltas)\n", + "\n", + "assert pvalue == pytest.approx(np_pvalues)" + ] + }, + { + "cell_type": "markdown", + "id": "39082d96", + "metadata": {}, + "source": [ + "test_paired_permutation_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39f3a041", + "metadata": {}, + "outputs": [], + "source": [ + "delta_delta = paired.mean_diff.delta_delta\n", + "pvalue = delta_delta.pvalue_permutation\n", + "permutations_delta_delta = delta_delta.permutations_delta_delta\n", + "\n", + "perm_test_1 = PermutationTest(df_test_treatment1[\"T1\"], \n", + " df_test_treatment1[\"T2\"], \n", + " effect_size=\"mean_diff\", \n", + " is_paired=\"sequential\")\n", + "perm_test_2 = PermutationTest(df_test_control[\"T1\"], \n", + " df_test_control[\"T2\"], \n", + " effect_size=\"mean_diff\", \n", + " is_paired=\"sequential\")\n", + "permutations_1 = perm_test_1.permutations\n", + "permutations_2 = perm_test_2.permutations\n", + "\n", + "delta_deltas = permutations_2-permutations_1\n", + "assert permutations_delta_delta == pytest.approx(delta_deltas)\n", + "\n", + "diff1 = np.mean(df_test_treatment1[\"T2\"]-df_test_treatment1[\"T1\"])\n", + "diff2 = np.mean(df_test_control[\"T2\"]-df_test_control[\"T1\"])\n", + "np_diff = diff2-diff1\n", + "\n", + "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_diff), \n", + " delta_deltas)))/len(delta_deltas)\n", + "\n", + "assert pvalue == pytest.approx(np_pvalues)" + ] + }, + { + "cell_type": "markdown", + "id": "b033f55a", + "metadata": {}, + "source": [ + "test_paired_specified_level_permutation_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6310737", + "metadata": {}, + "outputs": [], + "source": [ + "delta_delta = paired_specified_level.mean_diff.delta_delta\n", + "pvalue = delta_delta.pvalue_permutation\n", + "permutations_delta_delta = delta_delta.permutations_delta_delta\n", + "\n", + "perm_test_1 = PermutationTest(df_test_control[\"T2\"], \n", + " df_test_control[\"T1\"], \n", + " effect_size=\"mean_diff\", \n", + " is_paired=\"sequential\")\n", + "perm_test_2 = PermutationTest(df_test_treatment1[\"T2\"], \n", + " df_test_treatment1[\"T1\"], \n", + " effect_size=\"mean_diff\", \n", + " is_paired=\"sequential\")\n", + "permutations_1 = perm_test_1.permutations\n", + "permutations_2 = perm_test_2.permutations\n", + "\n", + "delta_deltas = permutations_2-permutations_1\n", + "assert permutations_delta_delta == pytest.approx(delta_deltas)\n", + "\n", + "diff1 = np.mean(df_test_control[\"T1\"]-df_test_control[\"T2\"])\n", + "diff2 = np.mean(df_test_treatment1[\"T1\"]-df_test_treatment1[\"T2\"])\n", + "np_diff = diff2-diff1\n", + "\n", + "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_diff), \n", + " delta_deltas)))/len(delta_deltas)\n", + "\n", + "assert pvalue == pytest.approx(np_pvalues)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tests/test_07_delta-delta_plots.py b/nbs/tests/test_07_delta-delta_plots.py new file mode 100644 index 00000000..754c0689 --- /dev/null +++ b/nbs/tests/test_07_delta-delta_plots.py @@ -0,0 +1,155 @@ +import pytest +import numpy as np +import pandas as pd +from scipy.stats import norm + + +import matplotlib as mpl +mpl.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.ticker as Ticker + +from dabest._api import load + +def create_demo_dataset_delta(seed=9999, N=20): + + import numpy as np + import pandas as pd + from scipy.stats import norm # Used in generation of populations. + + np.random.seed(seed) # Fix the seed so the results are replicable. + # pop_size = 10000 # Size of each population. + + from scipy.stats import norm # Used in generation of populations. + + # Create samples + y = norm.rvs(loc=3, scale=0.4, size=N*4) + y[N:2*N] = y[N:2*N]+1 + y[2*N:3*N] = y[2*N:3*N]-0.5 + + # Add drug column + t1 = np.repeat('Placebo', N*2).tolist() + t2 = np.repeat('Drug', N*2).tolist() + treatment = t1 + t2 + + # Add a `rep` column as the first variable for the 2 replicates of experiments done + rep = [] + for i in range(N*2): + rep.append('Rep1') + rep.append('Rep2') + + # Add a `genotype` column as the second variable + wt = np.repeat('W', N).tolist() + mt = np.repeat('M', N).tolist() + wt2 = np.repeat('W', N).tolist() + mt2 = np.repeat('M', N).tolist() + + + genotype = wt + mt + wt2 + mt2 + + # Add an `id` column for paired data plotting. + id = list(range(0, N*2)) + id_col = id + id + + + # Combine all columns into a DataFrame. + df = pd.DataFrame({'ID' : id_col, + 'Rep' : rep, + 'Genotype' : genotype, + 'Treatment': treatment, + 'Y' : y + }) + return df + + +df = create_demo_dataset_delta() + + +unpaired = load(data = df, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, + experiment = "Treatment") + +unpaired_specified = load(data = df, x = ["Genotype", "Genotype"], y = "Y", + delta2 = True, experiment = "Treatment", + experiment_label = ["Drug", "Placebo"], + x1_level = ["M", "W"]) + +baseline = load(data = df, x = ["Treatment", "Rep"], y = "Y", delta2 = True, + experiment = "Genotype", + paired="baseline", id_col="ID") + +sequential = load(data = df, x = ["Treatment", "Rep"], y = "Y", delta2 = True, + experiment = "Genotype", + paired="sequential", id_col="ID") + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_47_cummings_unpaired_delta_delta_meandiff(): + return unpaired.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_48_cummings_sequential_delta_delta_meandiff(): + return sequential.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_49_cummings_baseline_delta_delta_meandiff(): + return baseline.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_50_delta_plot_ylabel(): + return baseline.mean_diff.plot(swarm_label="This is my\nrawdata", + contrast_label="The bootstrap\ndistribtions!", + delta2_label="This is delta!"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_51_delta_plot_change_palette_a(): + return sequential.mean_diff.plot(custom_palette="Dark2"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_52_delta_specified(): + return unpaired_specified.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_53_delta_change_ylims(): + return sequential.mean_diff.plot(swarm_ylim=(0, 9), + contrast_ylim=(-2, 2), + fig_size=(15,6)); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_54_delta_invert_ylim(): + return sequential.mean_diff.plot(contrast_ylim=(2, -2), + contrast_label="More negative is better!"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_55_delta_median_diff(): + return sequential.median_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_56_delta_cohens_d(): + return unpaired.cohens_d.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_57_delta_show_delta2(): + return unpaired.mean_diff.plot(show_delta2=False); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_58_delta_axes_invert_ylim(): + return unpaired.mean_diff.plot(delta2_ylim=(2, -2), + delta2_label="More negative is better!"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_59_delta_axes_invert_ylim_not_showing_delta2(): + return unpaired.mean_diff.plot(delta2_ylim=(2, -2), + delta2_label="More negative is better!", + show_delta2=False); \ No newline at end of file diff --git a/nbs/tests/test_08_mini_meta_pvals.ipynb b/nbs/tests/test_08_mini_meta_pvals.ipynb new file mode 100644 index 00000000..c5d58184 --- /dev/null +++ b/nbs/tests/test_08_mini_meta_pvals.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0fc777d3", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pytest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90ea3a40", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._stats_tools import effsize\n", + "from dabest._stats_tools import confint_2group_diff as ci2g\n", + "from dabest._classes import PermutationTest, Dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2538a232", + "metadata": {}, + "outputs": [], + "source": [ + "# Data for tests.\n", + "# See Oehlert, G. W. (2000). A First Course in Design \n", + "# and Analysis of Experiments (1st ed.). W. H. Freeman.\n", + "# from Problem 16.3 Pg 444.\n", + "\n", + "rep1_yes = [53.4,54.3,55.9,53.8,56.3,58.6]\n", + "rep1_no = [58.2,60.4,62.4,59.5,64.5,64.5]\n", + "rep2_yes = [46.5,57.2,57.4,51.1,56.9,60.2]\n", + "rep2_no = [49.2,61.6,57.2,51.3,66.8,62.7]\n", + "df_mini_meta = pd.DataFrame({\n", + " \"Rep1_Yes\":rep1_yes,\n", + " \"Rep1_No\" :rep1_no,\n", + " \"Rep2_Yes\":rep2_yes,\n", + " \"Rep2_No\" :rep2_no\n", + "})\n", + "N=6 # Size of each group\n", + "\n", + "\n", + "# kwargs for Dabest class init.\n", + "dabest_default_kwargs = dict(x=None, y=None, ci=95, \n", + " resamples=5000, random_seed=12345,\n", + " proportional=False, delta2=False, experiment=None, \n", + " experiment_label=None, x1_level=None, paired=None,\n", + " id_col=None\n", + " )\n", + "\n", + "\n", + "unpaired = Dabest(data = df_mini_meta, idx =((\"Rep1_No\", \"Rep1_Yes\"), \n", + " (\"Rep2_No\", \"Rep2_Yes\")), \n", + " mini_meta=True,\n", + " **dabest_default_kwargs)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "86994f88", + "metadata": {}, + "source": [ + "test_mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0368caad", + "metadata": {}, + "outputs": [], + "source": [ + "mean_diff = unpaired.mean_diff.results['difference'].to_list()\n", + "np_result = [np.mean(rep1_yes)-np.mean(rep1_no), \n", + " np.mean(rep2_yes)-np.mean(rep2_no)]\n", + "assert mean_diff == pytest.approx(np_result)" + ] + }, + { + "cell_type": "markdown", + "id": "7cf4d56d", + "metadata": {}, + "source": [ + "test_variances" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973b227a", + "metadata": {}, + "outputs": [], + "source": [ + "mini_meta_delta = unpaired.mean_diff.mini_meta_delta\n", + "\n", + "control_var = mini_meta_delta.control_var\n", + "np_control_var = [np.var(rep1_no, ddof=1),\n", + " np.var(rep2_no, ddof=1)]\n", + "assert control_var == pytest.approx(np_control_var)\n", + "\n", + "test_var = mini_meta_delta.test_var\n", + "np_test_var = [np.var(rep1_yes, ddof=1),\n", + " np.var(rep2_yes, ddof=1)]\n", + "assert test_var == pytest.approx(np_test_var)\n", + "\n", + "group_var = mini_meta_delta.group_var\n", + "np_group_var = [ci2g.calculate_group_var(control_var[i], N,\n", + " test_var[i], N)\n", + " for i in range(0, 2)]\n", + "assert group_var == pytest.approx(np_group_var)" + ] + }, + { + "cell_type": "markdown", + "id": "a2c934e5", + "metadata": {}, + "source": [ + "test_weighted_mean_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "258f4b73", + "metadata": {}, + "outputs": [], + "source": [ + "difference = unpaired.mean_diff.mini_meta_delta.difference\n", + "\n", + "np_means = [np.mean(rep1_yes)-np.mean(rep1_no), \n", + " np.mean(rep2_yes)-np.mean(rep2_no)]\n", + "np_var = [np.var(rep1_yes, ddof=1)/N+np.var(rep1_no, ddof=1)/N,\n", + " np.var(rep2_yes, ddof=1)/N+np.var(rep2_no, ddof=1)/N]\n", + "\n", + "np_difference = effsize.weighted_delta(np_means, np_var)\n", + "\n", + "assert difference == pytest.approx(np_difference)" + ] + }, + { + "cell_type": "markdown", + "id": "d3a468f5", + "metadata": {}, + "source": [ + "test_unpaired_permutation_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45056c5f", + "metadata": {}, + "outputs": [], + "source": [ + "mini_meta_delta = unpaired.mean_diff.mini_meta_delta\n", + "pvalue = mini_meta_delta.pvalue_permutation\n", + "permutations_delta = mini_meta_delta.permutations_weighted_delta\n", + "\n", + "perm_test_1 = PermutationTest(rep1_no, rep1_yes, \n", + " effect_size=\"mean_diff\", \n", + " is_paired=False)\n", + "perm_test_2 = PermutationTest(rep2_no, rep2_yes, \n", + " effect_size=\"mean_diff\", \n", + " is_paired=False)\n", + "permutations_1 = perm_test_1.permutations\n", + "permutations_2 = perm_test_2.permutations\n", + "permutations_1_var = perm_test_1.permutations_var\n", + "permutations_2_var = perm_test_2.permutations_var\n", + "\n", + "weight_1 = np.true_divide(1,permutations_1_var)\n", + "weight_2 = np.true_divide(1,permutations_2_var)\n", + "\n", + "weighted_deltas = (weight_1*permutations_1 + weight_2*permutations_2)/(weight_1+weight_2)\n", + "assert permutations_delta == pytest.approx(weighted_deltas)\n", + "\n", + "\n", + "np_means = [np.mean(rep1_yes)-np.mean(rep1_no), \n", + " np.mean(rep2_yes)-np.mean(rep2_no)]\n", + "np_var = [np.var(rep1_yes, ddof=1)/N+np.var(rep1_no, ddof=1)/N,\n", + " np.var(rep2_yes, ddof=1)/N+np.var(rep2_no, ddof=1)/N]\n", + "np_weight= np.true_divide(1, np_var)\n", + "\n", + "np_difference = np.sum(np_means*np_weight)/np.sum(np_weight)\n", + "\n", + "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_difference), \n", + " weighted_deltas)))/len(weighted_deltas)\n", + "\n", + "assert pvalue == pytest.approx(np_pvalues)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tests/test_09_mini_meta_plots.py b/nbs/tests/test_09_mini_meta_plots.py new file mode 100644 index 00000000..a09a2dcb --- /dev/null +++ b/nbs/tests/test_09_mini_meta_plots.py @@ -0,0 +1,132 @@ +import pytest +import numpy as np +import pandas as pd +from scipy.stats import norm + + +import matplotlib as mpl +mpl.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.ticker as Ticker + +from dabest._api import load + +def create_demo_dataset(seed=9999, N=20): + + import numpy as np + import pandas as pd + from scipy.stats import norm # Used in generation of populations. + + np.random.seed(9999) # Fix the seed so the results are replicable. + # pop_size = 10000 # Size of each population. + + # Create samples + c1 = norm.rvs(loc=3, scale=0.4, size=N) + c2 = norm.rvs(loc=3.5, scale=0.75, size=N) + c3 = norm.rvs(loc=3.25, scale=0.4, size=N) + + t1 = norm.rvs(loc=3.5, scale=0.5, size=N) + t2 = norm.rvs(loc=2.5, scale=0.6, size=N) + t3 = norm.rvs(loc=3, scale=0.75, size=N) + t4 = norm.rvs(loc=3.5, scale=0.75, size=N) + t5 = norm.rvs(loc=3.25, scale=0.4, size=N) + t6 = norm.rvs(loc=3.25, scale=0.4, size=N) + + + # Add a `gender` column for coloring the data. + females = np.repeat('Female', N/2).tolist() + males = np.repeat('Male', N/2).tolist() + gender = females + males + + # Add an `id` column for paired data plotting. + id_col = pd.Series(range(1, N+1)) + + # Combine samples and gender into a DataFrame. + df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, + 'Control 2' : c2, 'Test 2' : t2, + 'Control 3' : c3, 'Test 3' : t3, + 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6, + 'Gender' : gender, 'ID' : id_col + }) + + return df + + +df = create_demo_dataset() + + +unpaired = load(df, + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + mini_meta=True) + + +baseline = load(df, id_col = "ID", + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + paired = "baseline", mini_meta=True) + + +sequential = load(df, id_col = "ID", + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + paired = "sequential", mini_meta=True) + + + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_60_cummings_unpaired_mini_meta_meandiff(): + return unpaired.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_61_cummings_sequential_mini_meta_meandiff(): + return sequential.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_62_cummings_baseline_mini_meta_meandiff(): + return baseline.mean_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_63_mini_meta_plot_ylabel(): + return baseline.mean_diff.plot(swarm_label="This is my\nrawdata", + contrast_label="The bootstrap\ndistribtions!"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_64_mini_meta_plot_change_palette_a(): + return unpaired.mean_diff.plot(custom_palette="Dark2"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_65_mini_meta_dot_sizes(): + return sequential.mean_diff.plot(show_pairs=False,raw_marker_size=3, + es_marker_size=12); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_66_mini_meta_change_ylims(): + return sequential.mean_diff.plot(swarm_ylim=(0, 5), + contrast_ylim=(-2, 2), + fig_size=(15,6)); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_67_mini_meta_invert_ylim(): + return sequential.mean_diff.plot(contrast_ylim=(2, -2), + contrast_label="More negative is better!"); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_68_mini_meta_median_diff(): + return sequential.median_diff.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_69_mini_meta_cohens_d(): + return unpaired.cohens_d.plot(); + + +@pytest.mark.mpl_image_compare(tolerance=10) +def test_70_mini_meta_not_show(): + return unpaired.mean_diff.plot(show_mini_meta=False); diff --git a/nbs/tests/test_10_proportion_plot.py b/nbs/tests/test_10_proportion_plot.py new file mode 100644 index 00000000..a7113c60 --- /dev/null +++ b/nbs/tests/test_10_proportion_plot.py @@ -0,0 +1,249 @@ +import pytest +import numpy as np +from scipy.stats import norm +import pandas as pd +import matplotlib as mpl +mpl.use('Agg') +import matplotlib.ticker as Ticker +import matplotlib.pyplot as plt + +from dabest._api import load + +def create_demo_prop_dataset(seed=9999, N=40): + + np.random.seed(9999) # Fix the seed so the results are replicable. + # Create samples + n = 1 + c1 = np.random.binomial(n, 0.2, size=N) + c2 = np.random.binomial(n, 0.2, size=N) + c3 = np.random.binomial(n, 0.8, size=N) + + t1 = np.random.binomial(n, 0.5, size=N) + t2 = np.random.binomial(n, 0.2, size=N) + t3 = np.random.binomial(n, 0.3, size=N) + t4 = np.random.binomial(n, 0.4, size=N) + t5 = np.random.binomial(n, 0.5, size=N) + t6 = np.random.binomial(n, 0.6, size=N) + + # Add a `gender` column for coloring the data. + females = np.repeat('Female', N / 2).tolist() + males = np.repeat('Male', N / 2).tolist() + gender = females + males + + # Add an `id` column for paired data plotting. + id_col = pd.Series(range(1, N + 1)) + + # Combine samples and gender into a DataFrame. + df = pd.DataFrame({'Control 1': c1, 'Test 1': t1, + 'Control 2': c2, 'Test 2': t2, + 'Control 3': c3, 'Test 3': t3, + 'Test 4': t4, 'Test 5': t5, 'Test 6': t6, + 'Gender': gender, 'ID': id_col + }) + + return df + + +df = create_demo_prop_dataset() + +two_groups_unpaired = load(df, idx=("Control 1", "Test 1"), proportional=True) + +multi_2group = load(df, idx=(("Control 1", "Test 1",), + ("Control 2", "Test 2")), + proportional=True) + +shared_control = load(df, idx=("Control 1", "Test 1", + "Test 2", "Test 3", + "Test 4", "Test 5", "Test 6"), + proportional=True) + +multi_groups = load(df, idx=(("Control 1", "Test 1",), + ("Control 2", "Test 2","Test 3"), + ("Control 3", "Test 4","Test 5", "Test 6") + ),proportional=True) + +two_groups_paired = load(df, idx=("Control 1", "Test 1"), + paired="baseline", id_col="ID",proportional=True) + +multi_2group_paired = load(df, idx=(("Control 1", "Test 1"), + ("Control 2", "Test 2")), + paired="baseline", id_col="ID", proportional=True) + +multi_groups_paired = load(df, idx=(("Control 1", "Test 1",), + ("Control 2", "Test 2","Test 3"), + ("Control 3", "Test 4","Test 5", "Test 6") + ),paired="baseline", id_col="ID", proportional=True) + +two_groups_sequential = load(df, idx=("Control 1", "Test 1"), + paired="sequential", id_col="ID",proportional=True) + +multi_2group_sequential = load(df, idx=(("Control 1", "Test 1"), + ("Control 2", "Test 2")), + paired="sequential", id_col="ID", proportional=True) + +multi_groups_sequential = load(df, idx=(("Control 1", "Test 1",), + ("Control 2", "Test 2","Test 3"), + ("Control 3", "Test 4","Test 5", "Test 6") + ),paired="sequential", id_col="ID", proportional=True) + + +@pytest.mark.mpl_image_compare +def test_101_gardner_altman_unpaired_propdiff(): + return two_groups_unpaired.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_103_cummings_two_group_unpaired_propdiff(): + return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), + float_contrast=False); + +@pytest.mark.mpl_image_compare +def test_105_cummings_multi_group_unpaired_propdiff(): + return multi_2group.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_106_cummings_shared_control_propdiff(): + return shared_control.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_107_cummings_multi_groups_propdiff(): + return multi_groups.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_109_gardner_altman_ylabel(): + return two_groups_unpaired.mean_diff.plot(bar_label="This is my\nrawdata", + contrast_label="The bootstrap\ndistribtions!"); + +@pytest.mark.mpl_image_compare +def test_110_change_fig_size(): + return two_groups_unpaired.mean_diff.plot(fig_size=(6, 6), + custom_palette="Dark2"); + +@pytest.mark.mpl_image_compare +def test_111_change_palette_b(): + return multi_2group.mean_diff.plot(custom_palette="Paired"); + + +my_color_palette = {"Control 1" : "blue", + "Test 1" : "purple", + "Control 2" : "#cb4b16", # This is a hex string. + "Test 2" : (0., 0.7, 0.2) # This is a RGB tuple. + } + +@pytest.mark.mpl_image_compare +def test_112_change_palette_c(): + return multi_2group.mean_diff.plot(custom_palette=my_color_palette); + +@pytest.mark.mpl_image_compare +def test_113_desat(): + return multi_2group.mean_diff.plot(custom_palette=my_color_palette, + bar_desat=0.1, + halfviolin_desat=0.25); + +@pytest.mark.mpl_image_compare +def test_114_change_ylims(): + return multi_2group.mean_diff.plot(contrast_ylim=(-2, 2)); + +@pytest.mark.mpl_image_compare +def test_115_invert_ylim(): + return multi_2group.mean_diff.plot(contrast_ylim=(2, -2), + contrast_label="More negative is better!"); + +@pytest.mark.mpl_image_compare +def test_116_ticker_gardner_altman(): + + fig = two_groups_unpaired.mean_diff.plot() + + rawswarm_axes = fig.axes[0] + contrast_axes = fig.axes[1] + + rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) + rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) + + contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) + contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) + return fig + +@pytest.mark.mpl_image_compare +def test_117_err_color(): + return two_groups_unpaired.mean_diff.plot(err_color="purple"); + +@pytest.mark.mpl_image_compare +def test_118_cummings_two_group_unpaired_meandiff_bar_width(): + return two_groups_unpaired.mean_diff.plot(bar_width=0.4,float_contrast=False); + +np.random.seed(9999) +Ns = [20, 10, 21, 20] +n=1 +c1 = pd.DataFrame({'Control':np.random.binomial(n, 0.2, size=Ns[0])}) +t1 = pd.DataFrame({'Test 1': np.random.binomial(n, 0.5, size=Ns[1])}) +t2 = pd.DataFrame({'Test 2': np.random.binomial(n, 0.4, size=Ns[2])}) +t3 = pd.DataFrame({'Test 3': np.random.binomial(n, 0.7, size=Ns[3])}) +wide_df = pd.concat([c1, t1, t2, t3],axis=1) + + +long_df = pd.melt(wide_df, + value_vars=["Control", "Test 1", "Test 2", "Test 3"], + value_name="value", + var_name="group") +long_df['dummy'] = np.repeat(np.nan, len(long_df)) + +@pytest.mark.mpl_image_compare +def test_119_wide_df_nan(): + + wide_df_dabest = load(wide_df, + idx=("Control", "Test 1", "Test 2", "Test 3"), + proportional=True + ) + + return wide_df_dabest.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_120_long_df_nan(): + + long_df_dabest = load(long_df, x="group", y="value", + idx=("Control", "Test 1", "Test 2", "Test 3"), + proportional=True + ) + + return long_df_dabest.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_121_cohens_h_gardner_altman(): + return two_groups_unpaired.cohens_h.plot(); + +@pytest.mark.mpl_image_compare +def test_122_cohens_h_cummings(): + return two_groups_unpaired.cohens_h.plot(float_contrast=False); + +@pytest.mark.mpl_image_compare +def test_123_sankey_gardner_altman(): + return two_groups_paired.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_124_sankey_cummings(): + return two_groups_paired.mean_diff.plot(float_contrast=False); + +@pytest.mark.mpl_image_compare +def test_125_sankey_2paired_groups(): + return multi_2group_paired.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_126_sankey_2sequential_groups(): + return multi_2group_sequential.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_127_sankey_multi_group_paired(): + return multi_groups_paired.mean_diff.plot(); + +@pytest.mark.mpl_image_compare +def test_128_sankey_transparency(): + return two_groups_paired.mean_diff.plot(sankey_kwargs = {"alpha": 0.2}); + + +@pytest.mark.mpl_image_compare +def test_129_style_sheets(): + # Perform this test last so we don't have to reset the plot style. + plt.style.use("dark_background") + return multi_2group.mean_diff.plot(face_color="black"); + + diff --git a/nbs/tests/test_99_confint.ipynb b/nbs/tests/test_99_confint.ipynb new file mode 100644 index 00000000..90557586 --- /dev/null +++ b/nbs/tests/test_99_confint.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a3d966b3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.stats import norm\n", + "from scipy.stats import skewnorm\n", + "import pandas as pd\n", + "import pytest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9920ab6c", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._api import load" + ] + }, + { + "cell_type": "markdown", + "id": "fa5cc9c1", + "metadata": {}, + "source": [ + "test_paired_mean_diff_ci" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c39d5daa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhang\\anaconda3\\lib\\site-packages\\scipy\\stats\\_morestats.py:3337: UserWarning: Exact p-value calculation does not work if there are zeros. Switching to normal approximation.\n", + " warnings.warn(\"Exact p-value calculation does not work if there are \"\n" + ] + } + ], + "source": [ + "# See Altman et al., Statistics with Confidence: \n", + "# Confidence Intervals and Statistical Guidelines (Second Edition). Wiley, 2000.\n", + "# Pg 31.\n", + "# Added in v0.2.5.\n", + "blood_pressure = {\"before\": [148, 142, 136, 134, 138, 140, 132, 144,\n", + " 128, 170, 162, 150, 138, 154, 126, 116],\n", + " \"after\" : [152, 152, 134, 148, 144, 136, 144, 150, \n", + " 146, 174, 162, 162, 146, 156, 132, 126],\n", + " \"subject_id\" : np.arange(1, 17)}\n", + "exercise_bp = pd.DataFrame(blood_pressure)\n", + "\n", + "\n", + "ex_bp = load(data=exercise_bp, idx=(\"before\", \"after\"), \n", + " paired=\"baseline\", id_col=\"subject_id\")\n", + "paired_mean_diff = ex_bp.mean_diff.results\n", + "\n", + "assert pytest.approx(3.875) == paired_mean_diff.bca_low[0]\n", + "assert pytest.approx(9.5) == paired_mean_diff.bca_high[0]" + ] + }, + { + "cell_type": "markdown", + "id": "de5c07cc", + "metadata": {}, + "source": [ + "test_unpaired_ci" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11e82b97", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:1663: UserWarning: The lower limit of the interval was in the bottom 10 values. The result should be considered unstable.\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n" + ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'dabest.effsize'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2621\u001b[0m, in \u001b[0;36mEffectSizeDataFrame.results\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__results\u001b[49m\n\u001b[0;32m 2622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n", + "\u001b[1;31mAttributeError\u001b[0m: 'EffectSizeDataFrame' object has no attribute '_EffectSizeDataFrame__results'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 79\u001b[0m\n\u001b[0;32m 76\u001b[0m load_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(ci\u001b[38;5;241m=\u001b[39mci, random_seed\u001b[38;5;241m=\u001b[39mrnd_sd)\n\u001b[0;32m 78\u001b[0m std_diff_data \u001b[38;5;241m=\u001b[39m load(data\u001b[38;5;241m=\u001b[39mstd_diff_df, idx\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mControl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest\u001b[39m\u001b[38;5;124m\"\u001b[39m), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mload_kwargs)\n\u001b[1;32m---> 79\u001b[0m cd \u001b[38;5;241m=\u001b[39m \u001b[43mstd_diff_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcohens_d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresults\u001b[49m\n\u001b[0;32m 80\u001b[0m \u001b[38;5;66;03m# print(\"cohen's d\") # for debug.\u001b[39;00m\n\u001b[0;32m 81\u001b[0m cd_low, cd_high \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(cd\u001b[38;5;241m.\u001b[39mbca_low), \u001b[38;5;28mfloat\u001b[39m(cd\u001b[38;5;241m.\u001b[39mbca_high)\n", + "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2623\u001b[0m, in \u001b[0;36mEffectSizeDataFrame.results\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__results\n\u001b[0;32m 2622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m-> 2623\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pre_calc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2624\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__results\n", + "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2233\u001b[0m, in \u001b[0;36mEffectSizeDataFrame.__pre_calc\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2230\u001b[0m control \u001b[38;5;241m=\u001b[39m dat[dat[xvar] \u001b[38;5;241m==\u001b[39m cname][yvar]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 2231\u001b[0m test \u001b[38;5;241m=\u001b[39m dat[dat[xvar] \u001b[38;5;241m==\u001b[39m tname][yvar]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m-> 2233\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mTwoGroupsEffectSize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontrol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2234\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__effect_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2235\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__proportional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2236\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__is_paired\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2237\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__ci\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2238\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__resamples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2239\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__permutation_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2240\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__random_seed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2241\u001b[0m r_dict \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mto_dict()\n\u001b[0;32m 2242\u001b[0m r_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontrol\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m cname\n", + "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:1698\u001b[0m, in \u001b[0;36mTwoGroupsEffectSize.__init__\u001b[1;34m(self, control, test, effect_size, proportional, is_paired, ci, resamples, permutation_count, random_seed)\u001b[0m\n\u001b[0;32m 1694\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pct_high \u001b[38;5;241m=\u001b[39m sorted_bootstraps[pct_idx_high]\n\u001b[0;32m 1696\u001b[0m \u001b[38;5;66;03m# Perform statistical tests.\u001b[39;00m\n\u001b[1;32m-> 1698\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__PermutationTest_result \u001b[38;5;241m=\u001b[39m \u001b[43mPermutationTest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontrol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 1699\u001b[0m \u001b[43m \u001b[49m\u001b[43meffect_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 1700\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_paired\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1701\u001b[0m \u001b[43m \u001b[49m\u001b[43mpermutation_count\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_paired \u001b[38;5;129;01mand\u001b[39;00m proportional \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 1704\u001b[0m \u001b[38;5;66;03m# Wilcoxon, a non-parametric version of the paired T-test.\u001b[39;00m\n\u001b[0;32m 1705\u001b[0m wilcoxon \u001b[38;5;241m=\u001b[39m spstats\u001b[38;5;241m.\u001b[39mwilcoxon(control, test)\n", + "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2820\u001b[0m, in \u001b[0;36mPermutationTest.__init__\u001b[1;34m(self, control, test, effect_size, is_paired, permutation_count, random_seed, **kwargs)\u001b[0m\n\u001b[0;32m 2818\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m 2819\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrandom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PCG64, RandomState\n\u001b[1;32m-> 2820\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meffsize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m two_group_difference\n\u001b[0;32m 2821\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfint_2group_diff\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m calculate_group_var\n\u001b[0;32m 2824\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__permutation_count \u001b[38;5;241m=\u001b[39m permutation_count\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'dabest.effsize'" + ] + } + ], + "source": [ + "# Dropped to 30 reps to save time. v0.2.5.\n", + "reps=30\n", + "ci=95\n", + "POPULATION_N = 10000\n", + "SAMPLE_N = 10\n", + "\n", + "# Create data for hedges g and cohens d.\n", + "CONTROL_MEAN = np.random.randint(1, 1000)\n", + "POP_SD = np.random.randint(1, 15)\n", + "POP_D = np.round(np.random.uniform(-2, 2, 1)[0], 2)\n", + "\n", + "TRUE_STD_DIFFERENCE = CONTROL_MEAN + (POP_D * POP_SD)\n", + "norm_sample_kwargs = dict(scale=POP_SD, size=SAMPLE_N)\n", + "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", + "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_STD_DIFFERENCE, **norm_sample_kwargs)\n", + "\n", + "std_diff_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", + "\n", + "\n", + "\n", + "# Create mean_diff data\n", + "CONTROL_MEAN = np.random.randint(1, 1000)\n", + "POP_SD = np.random.randint(1, 15)\n", + "TRUE_DIFFERENCE = np.random.randint(-POP_SD*5, POP_SD*5)\n", + "\n", + "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", + "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_DIFFERENCE, **norm_sample_kwargs)\n", + "\n", + "mean_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", + "\n", + "\n", + "\n", + "# Create median_diff data\n", + "MEDIAN_DIFFERENCE = np.random.randint(-5, 5)\n", + "A = np.random.randint(-7, 7)\n", + "\n", + "skew_kwargs = dict(a=A, scale=5, size=POPULATION_N)\n", + "skewpop1 = skewnorm.rvs(**skew_kwargs, loc=100)\n", + "skewpop2 = skewnorm.rvs(**skew_kwargs, loc=100+MEDIAN_DIFFERENCE)\n", + "\n", + "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", + "skewsample1 = np.random.choice(skewpop1, **sample_kwargs)\n", + "skewsample2 = np.random.choice(skewpop2, **sample_kwargs)\n", + "\n", + "median_df = pd.DataFrame({'Control' : skewsample1, 'Test': skewsample2})\n", + "\n", + "\n", + "\n", + "# Create two populations with a 50% overlap.\n", + "CD_DIFFERENCE = np.random.randint(1, 10)\n", + "SD = np.abs(CD_DIFFERENCE)\n", + "\n", + "pop_kwargs = dict(scale=SD, size=POPULATION_N)\n", + "pop1 = norm.rvs(loc=100, **pop_kwargs)\n", + "pop2 = norm.rvs(loc=100+CD_DIFFERENCE, **pop_kwargs)\n", + "\n", + "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", + "sample1 = np.random.choice(pop1, **sample_kwargs)\n", + "sample2 = np.random.choice(pop2, **sample_kwargs)\n", + "\n", + "cd_df = pd.DataFrame({'Control' : sample1, 'Test': sample2})\n", + "\n", + "\n", + "\n", + "# Create several CIs and see if the true population difference lies within.\n", + "error_count_cohens_d = 0\n", + "error_count_hedges_g = 0\n", + "error_count_mean_diff = 0\n", + "error_count_median_diff = 0\n", + "error_count_cliffs_delta = 0\n", + "\n", + "for i in range(0, reps):\n", + " # print(i) # for debug.\n", + " # pick a random seed\n", + " rnd_sd = np.random.randint(0, 999999)\n", + " load_kwargs = dict(ci=ci, random_seed=rnd_sd)\n", + "\n", + " std_diff_data = load(data=std_diff_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", + " cd = std_diff_data.cohens_d.results\n", + " # print(\"cohen's d\") # for debug.\n", + " cd_low, cd_high = float(cd.bca_low), float(cd.bca_high)\n", + " if cd_low < POP_D < cd_high is False:\n", + " error_count_cohens_d += 1\n", + "\n", + " hg = std_diff_data.hedges_g.results\n", + " # print(\"hedges' g\") # for debug.\n", + " hg_low, hg_high = float(hg.bca_low), float(hg.bca_high)\n", + " if hg_low < POP_D < hg_high is False:\n", + " error_count_hedges_g += 1\n", + "\n", + "\n", + " mean_diff_data = load(data=mean_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", + " mean_d = mean_diff_data.mean_diff.results\n", + " # print(\"mean diff\") # for debug.\n", + " mean_d_low, mean_d_high = float(mean_d.bca_low), float(mean_d.bca_high)\n", + " if mean_d_low < TRUE_DIFFERENCE < mean_d_high is False:\n", + " error_count_mean_diff += 1\n", + "\n", + "\n", + " median_diff_data = load(data=median_df, idx=(\"Control\", \"Test\"),\n", + " **load_kwargs)\n", + " median_d = median_diff_data.median_diff.results\n", + " # print(\"median diff\") # for debug.\n", + " median_d_low, median_d_high = float(median_d.bca_low), float(median_d.bca_high)\n", + " if median_d_low < MEDIAN_DIFFERENCE < median_d_high is False:\n", + " error_count_median_diff += 1\n", + "\n", + "\n", + " cd_data = load(data=cd_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", + " cliffs = cd_data.cliffs_delta.results\n", + " # print(\"cliff's delta\") # for debug.\n", + " low, high = float(cliffs.bca_low), float(cliffs.bca_high)\n", + " if low < 0.5 < high is False:\n", + " error_count_cliffs_delta += 1\n", + "\n", + "\n", + "max_errors = int(np.ceil(reps * (100 - ci) / 100))\n", + "\n", + "assert error_count_cohens_d <= max_errors\n", + "assert error_count_hedges_g <= max_errors\n", + "assert error_count_mean_diff <= max_errors\n", + "assert error_count_median_diff <= max_errors\n", + "assert error_count_cliffs_delta <= max_errors\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9da1b76d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/01-basics.ipynb b/nbs/tutorials/01-basics.ipynb new file mode 100644 index 00000000..5816c471 --- /dev/null +++ b/nbs/tutorials/01-basics.ipynb @@ -0,0 +1,1465 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5b32febf", + "metadata": {}, + "source": [ + "# Basics\n", + "\n", + "> An end-to-end tutorial on how to use the dabest.\n", + "\n", + "- order: 1" + ] + }, + { + "cell_type": "markdown", + "id": "c964abcb", + "metadata": {}, + "source": [ + "## Load Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38b902ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We're using DABEST v2023.02.14\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "print(\"We're using DABEST v{}\".format(dabest.__version__))" + ] + }, + { + "cell_type": "markdown", + "id": "61f4ab6b", + "metadata": {}, + "source": [ + "## Create dataset for demo" + ] + }, + { + "cell_type": "markdown", + "id": "c45f63cd", + "metadata": {}, + "source": [ + "Here, we create a dataset to illustrate how ``dabest`` functions. In\n", + "this dataset, each column corresponds to a group of observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c459d31", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm # Used in generation of populations.\n", + "\n", + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "# pop_size = 10000 # Size of each population.\n", + "Ns = 20 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns/2).tolist()\n", + "males = np.repeat('Male', Ns/2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns+1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3,\n", + " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", + " 'Gender' : gender, 'ID' : id_col\n", + " })" + ] + }, + { + "cell_type": "markdown", + "id": "51097f12", + "metadata": {}, + "source": [ + "Note that we have 9 groups (3 Control samples and 6 Test samples). Our\n", + "dataset also has a non\\-numerical column indicating gender, and another\n", + "column indicating the identity of each observation." + ] + }, + { + "cell_type": "markdown", + "id": "e975d14a", + "metadata": {}, + "source": [ + "This is known as a 'wide' dataset. See this \n", + "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/) \n", + "for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48be62cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Test 4 \\\n", + "0 2.793984 3.420875 3.324661 1.707467 3.816940 1.796581 4.440050 \n", + "1 3.236759 3.467972 3.685186 1.121846 3.750358 3.944566 3.723494 \n", + "2 3.019149 4.377179 5.616891 3.301381 2.945397 2.832188 3.214014 \n", + "3 2.804638 4.564780 2.773152 2.534018 3.575179 3.048267 4.968278 \n", + "4 2.858019 3.220058 2.550361 2.796365 3.692138 3.276575 2.662104 \n", + "\n", + " Test 5 Test 6 Gender ID \n", + "0 2.937284 3.486127 Female 1 \n", + "1 2.837062 2.338094 Female 2 \n", + "2 3.111950 3.270897 Female 3 \n", + "3 3.743378 3.151188 Female 4 \n", + "4 2.977341 2.328601 Female 5 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "7dd2c3f4", + "metadata": {}, + "source": [ + "## Loading Data" + ] + }, + { + "cell_type": "markdown", + "id": "eda4a39f", + "metadata": {}, + "source": [ + "Before we create estimation plots and obtain confidence intervals for\n", + "our effect sizes, we need to load the data and the relevant groups.\n", + "\n", + "We simply supply the DataFrame to ``dabest.load()``. We also must supply\n", + "the two groups you want to compare in the ``idx`` argument as a tuple or\n", + "list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfb7a0a1", + "metadata": {}, + "outputs": [], + "source": [ + "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000)" + ] + }, + { + "cell_type": "markdown", + "id": "3befeecd", + "metadata": {}, + "source": [ + "Calling this ``Dabest`` object gives you a gentle greeting, as well as\n", + "the comparisons that can be computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4503730b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:20 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired" + ] + }, + { + "cell_type": "markdown", + "id": "3565f8d1", + "metadata": {}, + "source": [ + "### Changing statistical parameters" + ] + }, + { + "cell_type": "markdown", + "id": "f71a2c3d", + "metadata": {}, + "source": [ + "You can change the width of the confidence interval that will be\n", + "produced by manipulating the ``ci`` argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "407f6d9b", + "metadata": {}, + "outputs": [], + "source": [ + "two_groups_unpaired_ci90 = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), ci=90)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aeb436f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:23 2023.\n", + "\n", + "Effect size(s) with 90% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired_ci90" + ] + }, + { + "cell_type": "markdown", + "id": "5084cfcb", + "metadata": {}, + "source": [ + "## Effect sizes" + ] + }, + { + "cell_type": "markdown", + "id": "837ffe5c", + "metadata": {}, + "source": [ + "``dabest`` now features a range of effect sizes:\n", + " - the mean difference (``mean_diff``)\n", + " - the median difference (``median_diff``)\n", + " - [Cohen's d](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d) (``cohens_d``)\n", + " - [Hedges' g](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) (``hedges_g``)\n", + " - [Cliff's delta](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)(``cliffs_delta``)\n", + " \n", + " \n", + "Each of these are attributes of the ``Dabest`` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "782ff891", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:25 2023.\n", + "\n", + "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.mean_diff" + ] + }, + { + "cell_type": "markdown", + "id": "1ae5b84f", + "metadata": {}, + "source": [ + "For each comparison, the type of effect size is reported (here, it's the\n", + "\"unpaired mean difference\"). The confidence interval is reported as:\n", + "[*confidenceIntervalWidth* *LowerBound*, *UpperBound*]\n", + "\n", + "This confidence interval is generated through bootstrap resampling. See\n", + ":doc:`bootstraps` for more details.\n", + "\n", + "Since v0.3.0, DABEST will report the p-value of the [non-parametric two-sided approximate permutation t-test](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests). This is also known as the Monte Carlo permutation test.\n", + "\n", + "For unpaired comparisons, the p-values and test statistics of [Welch's t test](https://en.wikipedia.org/wiki/Welch%27s_t-test>), \n", + "[Student's t test](https://en.wikipedia.org/wiki/Student%27s_t-test), \n", + "and [Mann-Whitney U test](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test) can be found in addition. For paired comparisons, the p-values and test statistics of the \n", + "[paired Student's t](https://en.wikipedia.org/wiki/Student%27s_t-test#Paired_samples)\n", + "and [Wilcoxon](https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test) tests are presented.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f6884f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020mean differenceNone0.48029950.2208690.767721(140, 4889)0.2156970.761716(125, 4875)[0.6686169333655454, 0.4382051534234943, 0.665...500012345[-0.17259843762502491, 0.03802293852634886, -0...0.0015000[0.026356588154404337, 0.027102495439046997, 0...0.002094-3.3088060.002057-3.3088060.00162583.0
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" + ], + "text/plain": [ + " control test control_N test_N effect_size is_paired \\\n", + "0 Control 1 Test 1 20 20 mean difference None \n", + "\n", + " difference ci bca_low bca_high bca_interval_idx pct_low pct_high \\\n", + "0 0.48029 95 0.220869 0.767721 (140, 4889) 0.215697 0.761716 \n", + "\n", + " pct_interval_idx bootstraps \\\n", + "0 (125, 4875) [0.6686169333655454, 0.4382051534234943, 0.665... \n", + "\n", + " resamples random_seed permutations \\\n", + "0 5000 12345 [-0.17259843762502491, 0.03802293852634886, -0... \n", + "\n", + " pvalue_permutation permutation_count \\\n", + "0 0.001 5000 \n", + "\n", + " permutations_var pvalue_welch \\\n", + "0 [0.026356588154404337, 0.027102495439046997, 0... 0.002094 \n", + "\n", + " statistic_welch pvalue_students_t statistic_students_t \\\n", + "0 -3.308806 0.002057 -3.308806 \n", + "\n", + " pvalue_mann_whitney statistic_mann_whitney \n", + "0 0.001625 83.0 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.options.display.max_columns = 50\n", + "two_groups_unpaired.mean_diff.results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07ddee85", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highpvalue_permutationpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020mean differenceNone0.48029950.2208690.7677210.0010.002094-3.3088060.002057-3.3088060.00162583.0
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" + ], + "text/plain": [ + " control test control_N test_N effect_size is_paired \\\n", + "0 Control 1 Test 1 20 20 mean difference None \n", + "\n", + " difference ci bca_low bca_high pvalue_permutation pvalue_welch \\\n", + "0 0.48029 95 0.220869 0.767721 0.001 0.002094 \n", + "\n", + " statistic_welch pvalue_students_t statistic_students_t \\\n", + "0 -3.308806 0.002057 -3.308806 \n", + "\n", + " pvalue_mann_whitney statistic_mann_whitney \n", + "0 0.001625 83.0 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.mean_diff.statistical_tests" + ] + }, + { + "cell_type": "markdown", + "id": "2548d82c", + "metadata": {}, + "source": [ + "Let's compute the Hedges' *g* for our comparison." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e302c877", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:30 2023.\n", + "\n", + "The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.349, 1.62].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.hedges_g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3980ba80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020Hedges' gNone1.025525950.3493941.618579(42, 4724)0.4728441.74166(125, 4875)[1.1337301267831184, 0.8311210968422604, 1.539...500012345[-0.3295089865590538, 0.07158401210924781, -0....0.0015000[0.026356588154404337, 0.027102495439046997, 0...0.002094-3.3088060.002057-3.3088060.00162583.0
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" + ], + "text/plain": [ + " control test control_N test_N effect_size is_paired difference ci \\\n", + "0 Control 1 Test 1 20 20 Hedges' g None 1.025525 95 \n", + "\n", + " bca_low bca_high bca_interval_idx pct_low pct_high pct_interval_idx \\\n", + "0 0.349394 1.618579 (42, 4724) 0.472844 1.74166 (125, 4875) \n", + "\n", + " bootstraps resamples random_seed \\\n", + "0 [1.1337301267831184, 0.8311210968422604, 1.539... 5000 12345 \n", + "\n", + " permutations pvalue_permutation \\\n", + "0 [-0.3295089865590538, 0.07158401210924781, -0.... 0.001 \n", + "\n", + " permutation_count permutations_var \\\n", + "0 5000 [0.026356588154404337, 0.027102495439046997, 0... \n", + "\n", + " pvalue_welch statistic_welch pvalue_students_t statistic_students_t \\\n", + "0 0.002094 -3.308806 0.002057 -3.308806 \n", + "\n", + " pvalue_mann_whitney statistic_mann_whitney \n", + "0 0.001625 83.0 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.hedges_g.results" + ] + }, + { + "cell_type": "markdown", + "id": "5f1eb018", + "metadata": {}, + "source": [ + "## Producing estimation plots" + ] + }, + { + "cell_type": "markdown", + "id": "b451ab38", + "metadata": {}, + "source": [ + "To produce a **Gardner-Altman estimation plot**, simply use the\n", + "``.plot()`` method. You can read more about its genesis and design\n", + "inspiration at :doc:`robust-beautiful`.\n", + "\n", + "Every effect size instance has access to the ``.plot()`` method. This\n", + "means you can quickly create plots for different effect sizes easily." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a929da1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.hedges_g.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "5b566185", + "metadata": {}, + "source": [ + "Instead of a Gardner-Altman plot, you can produce a **Cumming estimation\n", + "plot** by setting ``float_contrast=False`` in the ``plot()`` method.\n", + "This will plot the bootstrap effect sizes below the raw data, and also\n", + "displays the the mean (gap) and ± standard deviation of each group\n", + "(vertical ends) as gapped lines. This design was inspired by Edward\n", + "Tufte's dictum to maximise the data-ink ratio." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4b10d17", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.hedges_g.plot(float_contrast=False);" + ] + }, + { + "cell_type": "markdown", + "id": "6af069aa", + "metadata": {}, + "source": [ + "The ``dabest`` package also implements a range of estimation plot\n", + "designs aimed at depicting common experimental designs.\n", + "\n", + "The **multi-two-group estimation plot** tiles two or more Cumming plots\n", + "horizontally, and is created by passing a *nested tuple* to ``idx`` when\n", + "``dabest.load()`` is first invoked.\n", + "\n", + "Thus, the lower axes in the Cumming plot is effectively a [forest\n", + "plot](https://en.wikipedia.org/wiki/Forest_plot), used in\n", + "meta-analyses to aggregate and compare data from different experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1caaaff8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n", + " (\"Control 2\", \"Test 2\")\n", + " ))\n", + "\n", + "multi_2group.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "cc59ca70", + "metadata": {}, + "source": [ + "The **shared control plot** displays another common experimental\n", + "paradigm, where several test samples are compared against a common\n", + "reference sample.\n", + "\n", + "This type of Cumming plot is automatically generated if the tuple passed\n", + "to ``idx`` has more than two data columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ab93ba9", + "metadata": {}, + "outputs": [], + "source": [ + "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\",\n", + " \"Test 4\", \"Test 5\", \"Test 6\")\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "688420ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:43 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "2. Test 2 minus Control 1\n", + "3. Test 3 minus Control 1\n", + "4. Test 4 minus Control 1\n", + "5. Test 5 minus Control 1\n", + "6. Test 6 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shared_control" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc3cc602", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:48 2023.\n", + "\n", + "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.914, -0.211].\n", + "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.295, 0.628].\n", + "The p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.306, 1.31].\n", + "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0137, 0.497].\n", + "The p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI -0.00441, 0.515].\n", + "The p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shared_control.mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "278fa389", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shared_control.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "0848f20b", + "metadata": {}, + "source": [ + "``dabest`` thus empowers you to robustly perform and elegantly present\n", + "complex visualizations and statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a638c960", + "metadata": {}, + "outputs": [], + "source": [ + "multi_groups = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n", + " (\"Control 2\", \"Test 2\",\"Test 3\"),\n", + " (\"Control 3\", \"Test 4\",\"Test 5\", \"Test 6\")\n", + " ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "854216aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:36:56 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "2. Test 2 minus Control 2\n", + "3. Test 3 minus Control 2\n", + "4. Test 4 minus Control 3\n", + "5. Test 5 minus Control 3\n", + "6. Test 6 minus Control 3\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multi_groups" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a3b6380", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:37:01 2023.\n", + "\n", + "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.895].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.3, -0.103].\n", + "The p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.114, 0.887].\n", + "The p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.404, 0.0742].\n", + "The p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.398, 0.102].\n", + "The p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multi_groups.mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c91c9d44", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_groups.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "66ca9423", + "metadata": {}, + "source": [ + "### Using long (aka 'melted') data frames" + ] + }, + { + "cell_type": "markdown", + "id": "1f532032", + "metadata": {}, + "source": [ + "``dabest`` can also work with 'melted' or 'long' data. This term is so\n", + "used because each row will now correspond to a single datapoint, with\n", + "one column carrying the value and other columns carrying 'metadata'\n", + "describing that datapoint.\n", + "\n", + "More details on wide vs long or 'melted' data can be found in this\n", + "[Wikipedia article](https://en.wikipedia.org/wiki/Wide_and_narrow_data). The\n", + "[pandas documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html)\n", + "gives recipes for melting dataframes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f529430a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GenderIDgroupmetric
0Female1Control 12.793984
1Female2Control 13.236759
2Female3Control 13.019149
3Female4Control 12.804638
4Female5Control 12.858019
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" + ], + "text/plain": [ + " Gender ID group metric\n", + "0 Female 1 Control 1 2.793984\n", + "1 Female 2 Control 1 3.236759\n", + "2 Female 3 Control 1 3.019149\n", + "3 Female 4 Control 1 2.804638\n", + "4 Female 5 Control 1 2.858019" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x='group'\n", + "y='metric'\n", + "\n", + "value_cols = df.columns[:-2] # select all but the \"Gender\" and \"ID\" columns.\n", + "\n", + "df_melted = pd.melt(df.reset_index(),\n", + " id_vars=[\"Gender\", \"ID\"],\n", + " value_vars=value_cols,\n", + " value_name=y,\n", + " var_name=x)\n", + "\n", + "df_melted.head() # Gives the first five rows of `df_melted`." + ] + }, + { + "cell_type": "markdown", + "id": "1ffb38fa", + "metadata": {}, + "source": [ + "When your data is in this format, you will need to specify the ``x`` and\n", + "``y`` columns in ``dabest.load()``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdee72da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:37:07 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analysis_of_long_df = dabest.load(df_melted, idx=(\"Control 1\", \"Test 1\"),\n", + " x=\"group\", y=\"metric\")\n", + "\n", + "analysis_of_long_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a6f8668", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "analysis_of_long_df.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5c9c8b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/02-repeated_measures.ipynb b/nbs/tutorials/02-repeated_measures.ipynb new file mode 100644 index 00000000..dca9afcd --- /dev/null +++ b/nbs/tutorials/02-repeated_measures.ipynb @@ -0,0 +1,587 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a4db386", + "metadata": {}, + "source": [ + "# Repeated Measures\n", + "\n", + "> Explanation of how to use dabest for repeated measures analysis.\n", + "\n", + "- order: 2" + ] + }, + { + "cell_type": "markdown", + "id": "9081a4df", + "metadata": {}, + "source": [ + "DABEST version 2023.02.14 expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test \n", + "groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.\n", + "\n", + "The repeated-measures function supports the calculation of effect sizes for\n", + "paired data, either based on sequential comparisons (group i vs group i + 1) \n", + "or baseline comparisons (control vs group i). To use these features, \n", + "you can simply declare the argument ``paired = \"sequential\"`` or ``paired = \"baseline\"`` \n", + "correspondingly while running ``dabest.load()``. As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the \n", + "``id_col`` keyword. \n", + "\n", + "**(Please note that** ``paired = True`` **and** ``paired = False`` **are no longer valid in v2023.02.14)**" + ] + }, + { + "cell_type": "markdown", + "id": "eecb3c0a", + "metadata": {}, + "source": [ + "## Load Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea25e869", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We're using DABEST v2023.02.14\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "print(\"We're using DABEST v{}\".format(dabest.__version__))" + ] + }, + { + "cell_type": "markdown", + "id": "1d78dd2c", + "metadata": {}, + "source": [ + "## Create dataset for demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9906d636", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm # Used in generation of populations.\n", + "\n", + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "# pop_size = 10000 # Size of each population.\n", + "Ns = 20 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns/2).tolist()\n", + "males = np.repeat('Male', Ns/2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns+1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3,\n", + " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", + " 'Gender' : gender, 'ID' : id_col\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76040145", + "metadata": {}, + "outputs": [], + "source": [ + "two_groups_paired_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", + " paired=\"sequential\", id_col=\"ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2774e88a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:39:03 2023.\n", + "\n", + "Paired effect size(s) for the sequential design of repeated-measures experiment \n", + "with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_paired_sequential" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6c78841", + "metadata": {}, + "outputs": [], + "source": [ + "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", + " paired=\"baseline\", id_col=\"ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c64388d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:39:04 2023.\n", + "\n", + "Paired effect size(s) for repeated measures against baseline \n", + "with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_paired_baseline" + ] + }, + { + "cell_type": "markdown", + "id": "17eae308", + "metadata": {}, + "source": [ + "When only 2 paired data groups are involved, assigning either ``baseline``\n", + "or ``sequential`` to ``paired`` will give you the same numerical results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27a891ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:39:08 2023.\n", + "\n", + "The paired mean difference for the sequential design of repeated-measures experiment \n", + "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_paired_sequential.mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb9f8761", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:39:09 2023.\n", + "\n", + "The paired mean difference for repeated measures against baseline \n", + "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_paired_baseline.mean_diff" + ] + }, + { + "cell_type": "markdown", + "id": "47395e35", + "metadata": {}, + "source": [ + "For paired data, we use\n", + "[slopegraphs](https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0003nk>)\n", + "(another innovation from Edward Tufte) to connect paired observations.\n", + "Both Gardner-Altman and Cumming plots support this.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10e1951a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_paired_baseline.mean_diff.plot(float_contrast=False);" + ] + }, + { + "cell_type": "markdown", + "id": "e86d261e", + "metadata": {}, + "source": [ + "You can also create repeated-measures plots with multiple test groups.In\n", + "this case, declaring ``paired`` to be ``sequential`` or ``baseline`` will\n", + "generate different results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59fdde69", + "metadata": {}, + "outputs": [], + "source": [ + "sequential_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n", + " paired=\"sequential\", id_col=\"ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3b41638", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:39:18 2023.\n", + "\n", + "The paired mean difference for the sequential design of repeated-measures experiment \n", + "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "The paired mean difference for the sequential design of repeated-measures experiment \n", + "between Test 1 and Test 2 is -1.02 [95%CI -1.36, -0.716].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "The paired mean difference for the sequential design of repeated-measures experiment \n", + "between Test 2 and Test 3 is 0.716 [95%CI 0.14, 1.22].\n", + "The p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequential_repeated_measures.mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1e57580", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sequential_repeated_measures.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "469088f9", + "metadata": {}, + "outputs": [], + "source": [ + "baseline_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n", + " paired=\"baseline\", id_col=\"ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fdd662a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:39:26 2023.\n", + "\n", + "The paired mean difference for repeated measures against baseline \n", + "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "The paired mean difference for repeated measures against baseline \n", + "between Control 1 and Test 2 is -0.542 [95%CI -0.975, -0.198].\n", + "The p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only. \n", + "\n", + "The paired mean difference for repeated measures against baseline \n", + "between Control 1 and Test 3 is 0.174 [95%CI -0.297, 0.706].\n", + "The p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_repeated_measures.mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c152456", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_baseline_repeated_measures = dabest.load(df, idx=((\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n", + " (\"Control 2\", \"Test 4\", \"Test 5\", \"Test 6\")),\n", + " paired=\"baseline\", id_col=\"ID\")\n", + "multi_baseline_repeated_measures.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f44b0ecf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/03-proportion_plot.ipynb b/nbs/tutorials/03-proportion_plot.ipynb new file mode 100644 index 00000000..5b1d9099 --- /dev/null +++ b/nbs/tutorials/03-proportion_plot.ipynb @@ -0,0 +1,1045 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "29d885e4", + "metadata": {}, + "source": [ + "# Proportion Plots\n", + "\n", + "> A guide to plot proportion plot with binary data.\n", + "\n", + "- order: 3" + ] + }, + { + "cell_type": "markdown", + "id": "098387ff", + "metadata": {}, + "source": [ + "As of v2023.02.14, DABEST can be used to produce Cohen's *h* and the corresponding proportion plot for binary data. It's important to note that the code we provide only supports numerical proportion data, \n", + "where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for \n", + "analyzing proportion data that contains non-numeric values, such as strings like 'yes' and 'no'.\n" + ] + }, + { + "cell_type": "markdown", + "id": "325366c2", + "metadata": {}, + "source": [ + "## Load libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04166b0a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We're using DABEST v2023.02.14\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "print(\"We're using DABEST v{}\".format(dabest.__version__))" + ] + }, + { + "cell_type": "markdown", + "id": "7942a214", + "metadata": {}, + "source": [ + "## Create dataset for demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fca1a99f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4010010001Female5
\n", + "
" + ], + "text/plain": [ + " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Test 4 Test 5 \\\n", + "0 1 1 0 0 1 0 0 1 \n", + "1 0 0 0 1 1 1 0 0 \n", + "2 0 0 0 0 1 0 1 1 \n", + "3 0 0 0 0 1 0 0 1 \n", + "4 0 1 0 0 1 0 0 0 \n", + "\n", + " Test 6 Gender ID \n", + "0 0 Female 1 \n", + "1 0 Female 2 \n", + "2 0 Female 3 \n", + "3 0 Female 4 \n", + "4 1 Female 5 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "Ns = 40 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "n = 1\n", + "c1 = np.random.binomial(n, 0.2, size=Ns)\n", + "c2 = np.random.binomial(n, 0.2, size=Ns)\n", + "c3 = np.random.binomial(n, 0.8, size=Ns)\n", + "\n", + "t1 = np.random.binomial(n, 0.5, size=Ns)\n", + "t2 = np.random.binomial(n, 0.2, size=Ns)\n", + "t3 = np.random.binomial(n, 0.3, size=Ns)\n", + "t4 = np.random.binomial(n, 0.4, size=Ns)\n", + "t5 = np.random.binomial(n, 0.5, size=Ns)\n", + "t6 = np.random.binomial(n, 0.6, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns / 2).tolist()\n", + "males = np.repeat('Male', Ns / 2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns + 1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n", + " 'Control 2': c2, 'Test 2': t2,\n", + " 'Control 3': c3, 'Test 3': t3,\n", + " 'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n", + " 'Gender': gender, 'ID': id_col\n", + " })\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b08c7276", + "metadata": {}, + "source": [ + "## Loading Data" + ] + }, + { + "cell_type": "markdown", + "id": "a10dd2b3", + "metadata": {}, + "source": [ + "When loading data, specify ``proportional=True``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85d38228", + "metadata": {}, + "outputs": [], + "source": [ + "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "092e4f0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:41:40 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired" + ] + }, + { + "cell_type": "markdown", + "id": "217bf60d", + "metadata": {}, + "source": [ + "## Effect sizes" + ] + }, + { + "cell_type": "markdown", + "id": "32a8ce9b", + "metadata": {}, + "source": [ + "For proportion plot, dabest features two effect sizes:\n", + " - the mean difference (``mean_diff``)\n", + " - [Cohen's h](https://en.wikipedia.org/wiki/Cohen%27s_h) (``cohens_h``)\n", + "\n", + "Each of these are attributes of the ``Dabest`` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9405f04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:42:28 2023.\n", + "\n", + "The unpaired mean difference between Control 1 and Test 1 is 0.375 [95%CI 0.15, 0.525].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.mean_diff" + ] + }, + { + "cell_type": "markdown", + "id": "103bfed3", + "metadata": {}, + "source": [ + "Let's compute the Cohen's h for our comparison." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "241db548", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:42:45 2023.\n", + "\n", + "The unpaired Cohen's h between Control 1 and Test 1 is 0.825 [95%CI 0.33, 1.22].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.cohens_h" + ] + }, + { + "cell_type": "markdown", + "id": "f8e4b193", + "metadata": {}, + "source": [ + "## Producing Proportional Plots" + ] + }, + { + "cell_type": "markdown", + "id": "66e29a7e", + "metadata": {}, + "source": [ + "To produce a **Gardner-Altman estimation plot**, simply use the\n", + "``.plot()`` method. \n", + "\n", + "Every effect size instance has access to the ``.plot()`` method. This\n", + "means you can quickly create plots for different effect sizes easily.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8c30a86", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.cohens_h.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "36f8b4c0", + "metadata": {}, + "source": [ + "The white part in the bar represents the proportion of observations in the dataset that do not belong to the category, which is \n", + "equivalent to the proportion of 0 in the data. The colored part, on the other hand, represents the proportion of observations \n", + "that belong to the category, which is equivalent to the proportion of 1 in the data. By default, the value of 'group_summaries' \n", + "is set to \"mean_sd\". This means that the error bars in the plot display the mean and ± standard deviation of each group as \n", + "gapped lines. The gap represents the mean, while the vertical ends represent the standard deviation. Alternatively, if the \n", + "value of 'group_summaries' is set to \"median_quartiles\", the median and 25th and 75th percentiles of each group are plotted instead. \n", + "By default, the bootstrap effect sizes is plotted on the right axis.\n", + "\n", + "Instead of a Gardner-Altman plot, you can produce a **Cumming estimation\n", + "plot** by setting ``float_contrast=False`` in the ``plot()`` method.\n", + "This will plot the bootstrap effect sizes below the raw data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4d75d09", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.mean_diff.plot(float_contrast=False);" + ] + }, + { + "cell_type": "markdown", + "id": "78740e4c", + "metadata": {}, + "source": [ + "You can also modify the width of bars as you expect by setting ``bar_width`` in the ``plot()`` method. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20997acf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Default is 0.8.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8903725f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.mean_diff.plot(bar_desat=1.0);" + ] + }, + { + "cell_type": "markdown", + "id": "2bd92f6a", + "metadata": {}, + "source": [ + "``bar_label`` and ``contrast_label`` can be used to set labels for the y-axis of the bar plot and the contrast plot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fdb7ad6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.mean_diff.plot(bar_label=\"success\",contrast_label=\"difference\");" + ] + }, + { + "cell_type": "markdown", + "id": "411d9947", + "metadata": {}, + "source": [ + "The color of error bar can be modified by setting 'err_color'.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef6ba800", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " control test control_N test_N effect_size is_paired difference ci \\\n", + "0 Control 1 Test 1 40 40 Cohen's h None 0.825418 95 \n", + "\n", + " bca_low bca_high ... pvalue_permutation permutation_count \\\n", + "0 0.329684 1.219937 ... 0.0 5000 \n", + "\n", + " permutations_var pvalue_welch \\\n", + "0 [0.011266025641025641, 0.011266025641025641, 0... 0.000289 \n", + "\n", + " statistic_welch pvalue_students_t statistic_students_t \\\n", + "0 -3.81474 0.000271 -3.81474 \n", + "\n", + " pvalue_mann_whitney statistic_mann_whitney proportional_difference \n", + "0 0.000434 500.0 0.825418 \n", + "\n", + "[1 rows x 28 columns]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_groups_unpaired.cohens_h.results" + ] + }, + { + "cell_type": "markdown", + "id": "845b7224", + "metadata": {}, + "source": [ + "## Producing estimation plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d0e1042", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.cohens_h.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "5f33004b", + "metadata": {}, + "source": [ + "The white part in the bar represents the proportion of observations in the dataset that do not belong to the category, which is \n", + "equivalent to the proportion of 0 in the data. The colored part, on the other hand, represents the proportion of observations \n", + "that belong to the category, which is equivalent to the proportion of 1 in the data. By default, the value of \"group_summaries\"\n", + "is set to \"mean_sd\". This means that the error bars in the plot display the mean and ± standard deviation of each group as \n", + "gapped lines. The gap represents the mean, while the vertical ends represent the standard deviation. Alternatively, if the \n", + "value of \"group_summaries\" is set to \"median_quartiles\", the median and 25th and 75th percentiles of each group are plotted instead. \n", + "By default, the bootstrap effect sizes is plotted on the right axis." + ] + }, + { + "cell_type": "markdown", + "id": "f3865a7a", + "metadata": {}, + "source": [ + "Instead of a Gardner-Altman plot, you can produce a **Cumming estimation\n", + "plot** by setting ``float_contrast=False`` in the ``plot()`` method.\n", + "This will plot the bootstrap effect sizes below the raw data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e8639a1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.mean_diff.plot(float_contrast=False);" + ] + }, + { + "cell_type": "markdown", + "id": "3e649272", + "metadata": {}, + "source": [ + "## Producing Paired Proportion Plots" + ] + }, + { + "cell_type": "markdown", + "id": "e6c37cd5", + "metadata": {}, + "source": [ + "For paired version of proportional plot, we adapt the style of Sankey Diagram. The width of each bar in each xticks represent \n", + "the proportion of corresponding label in the group, and the strip denotes the paired relationship for each observation.\n", + "\n", + "Similar to the unpaired version, the ``.plot()`` method is used to produce a **Gardner-Altman estimation plot**, the only difference is that\n", + "the ``is_paired`` parameter is set to either ``baseline`` or ``sequential`` when loading data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fafe0150", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), \n", + " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", + " \n", + "two_groups_baseline.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "6984eaf5", + "metadata": {}, + "source": [ + "The paired proportional plot also supports the ``float_contrast`` parameter, which can be set to ``False`` to produce a **Cumming estimation plot**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0c9d25e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_baseline.mean_diff.plot(float_contrast=False);" + ] + }, + { + "cell_type": "markdown", + "id": "c9cea250", + "metadata": {}, + "source": [ + "The upper part (grey part) of the bar represents the proportion of observations in the dataset that do not belong to the category, which is\n", + "equivalent to the proportion of 0 in the data. The lower part, on the other hand, represents the proportion of observations that belong to the category, which is\n", + "or **success**, which is equivalent to the proportion of 1 in the data. \n", + "\n", + "\n", + "Repeated measures is also supported in paired proportional plot, by changing the ``is_paired`` parameter, two types of plot can be produced.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e949b002", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_group_baseline = dabest.load(df, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),\n", + " (\"Test 4\", \"Test 5\", \"Test 6\"))),\n", + " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", + "\n", + "multi_group_baseline.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "611f1567", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_group_sequential = dabest.load(df, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),\n", + " (\"Test 4\", \"Test 5\", \"Test 6\"))),\n", + " proportional=True, paired=\"sequential\", id_col=\"ID\")\n", + "\n", + "multi_group_sequential.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "539a0a22", + "metadata": {}, + "source": [ + "From the above two images, we can see that the on both the observed value plot and delta plot, the pairs compared are different in terms of the paired settings. And for detailed information about repeated measures, please refer to :doc:`repeatedmeasures` .\n", + "\n", + "If you want to specify the order of the groups, you can use the ``idx`` parameter in the ``.load()`` method.\n", + "\n", + "For all the groups to be compared together, you can put all the groups in the ``idx`` parameter in the ``.load()`` method without subbrackets.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8b6e61c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_group_baseline_specify = dabest.load(df, idx=((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\",\n", + " \"Test 4\", \"Test 5\", \"Test 6\")),\n", + " proportional=True, paired=\"baseline\", id_col=\"ID\")\n", + "\n", + "multi_group_baseline_specify.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "e686109e", + "metadata": {}, + "source": [ + "Several exclusive parameters can be supplied to the ``plot()`` method to customize the paired proportional plot.\n", + "By updating the sankey_kwargs parameter, you can customize the Sankey plot. The following parameters are supported:\n", + "\n", + "- **width**: The width of each Sankey bar. Default is 0.5.\n", + "- **align**: The alignment of each Sankey bar. Default is \"center\".\n", + "- **alpha**: The transparency of each Sankey bar. Default is 0.4.\n", + "- **bar_width**: The width of each bar on the side in the plot. Default is 0.1.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7b25c1d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eHgBg9+7dhulZarW62a0WQ0RkLSUlJQ1aFIOkl5iYaLRgyIYNG/DOO+9YfF6zE/XAgQMxfvx4jB8/HufPnzfcqz5z5kyzXIOViMharl+/LnUI1ABr1qzB/fffX2N7p06dLF7sBLAgUa9YsQKRkZG4ceMGvv76a/j4+AAA0tLS8I9//MPiQIiImruGzLUl6WVnZyMwMLDGdj8/P2RlZVl8XrPvUXt5eeHDDz+ssX3+/PkWB0FERJVjgPR6PRQKs9tQJANBQUE4ePBgjdH7Bw8eRKtWrSw+r0Wfhv379+PZZ59FVFSU4Rfgpk2bcODAAYsDISJq7srLyxvU8iJpjR8/HtOmTcPHH3+MK1eu4MqVK9iwYQOmT5+OCRMmWHxes1vUX3/9NUaPHo1Ro0YhPT3dUCXl9u3bePvtt7Fz506LgyEiau4uXLiA1q1bSx0GWWDWrFnIz8/HpEmTUFFRAaByoPVrr71mdP1zU5ndol64cCFWr16Njz76CI6OjobtUVFRSE9PtzgQIiICLl++DI1GI3UYZAFBEPDOO+/gxo0bOHLkCH755Rfk5+fjzTffbNB5zW5Rnzt3Dn369Kmx3cPDA7du3WpQMEREzZ1Go8GFCxcQFhYmdShkITc3Nzz44INWO5/ZiTowMBC///57jalYBw4cQLt27awVFxFRs3XmzBmEhoZy8RM7U1xcjMWLF2PPnj3IycmpURHt0qVLFp3X7ET94osv4pVXXsGGDRsgCAKuX7+Ow4cPY8aMGQ1u3hMREXDz5k1cu3YNQUFBUodCZhg/fjxSU1MxevRoBAYGWu2HltmJetasWSgoKEC/fv1QVlaGPn36wMnJCTNmzMDLL79slaCIiJq7X375hYnaznz//ff473//i+joaKue1+xEDQCLFi3CnDlzcPbsWej1eoSFhRnqUhMRUcNdv34dN27cgJ+fn9ShkIlatGgBb29vq5/X7FHfBQUFyM/Ph4uLCyIiItCzZ0+4ubkhPz8fhYWFVg+QiKgpioiIQLdu3bBo0aJa9zl58mTjBUQN9tZbb+HNN99ESUmJVc9rdov673//O4YOHVqjnugXX3yBb775hvOoiYhMkJ2djaysLHh5edW6zx9//IGCggJ4eno2XmBksffeew8XL15EQEAA2rZtW20KMwCLpzCbnaiPHj2KpUuX1tj+8MMPY86cORYFQURENYmiiF9//RW9e/eWOhQywRNPPGGT85qdqMvLy6HVamts12g0LHNJRGRl58+fR3h4OFxcXKQOheqRkJBgk/OafY/6wQcfxNq1a2tsX716NcLDw60SFBERVdLpdDhz5ozUYZCJbt26hXXr1mH27NnIz88HUNnl3ZDKaGa3qBctWoQBAwbgl19+wd/+9jcAwJ49e/Dzzz9j9+7dFgdCRETGnTlzBl27doWTk5PUoVAdfv31VwwYMACenp74448/MGHCBHh7e2P79u24cuUKPv30U4vOa3aLOjo6GocPH0ZQUBC++OILfPvtt+jQoQPvoxAR2UhFRQXOnj0rdRhUj/j4eIwdOxYXLlyAWq02bB8yZAj27dtn8XktmkfdvXt3bNmyxeKLEhGReX799Vd06tQJKpVK6lCoFj///DPWrFlTY3vr1q2RnZ1t8XnNblHv3LkTu3btqrF9165d+P777y0OhIiIaldeXo5ffvlF6jCoDmq12uh6IufOnWvQwjVmJ+rXX38dOp2uxnZRFPH6669bHAgREdXt9OnTVl9Mg6zn8ccfx4IFCwxlSgVBQGZmJl5//XU89dRTFp/X7ERdW/m1+++/H7///rvFgRARUd00Gg2OHTsmdRhUi3fffRc3btyAv78/SktL0bdvX3To0AHu7u51rkBXH7PvUXt6euLSpUs1ylz+/vvvcHV1tTgQIiKq3/nz59GpUyeuAS5DHh4eOHDgAPbu3Yv09HTo9Xo88MADGDBgQIPOa3aiHjZsGKZNm4bt27ejffv2ACqT9Kuvvophw4Y1KBgiIqrfgQMH8MQTT7BetYxotVqo1WqcPHkS/fv3R//+/a12brO7vv/1r3/B1dUV999/P0JCQhASEoLQ0FD4+Pjg3XfftVpgRERk3I0bN/Dbb79JHQbdwcHBAcHBwUbHcDX43OYe4OnpiUOHDiElJQW//PILnJ2d0bVrV/Tp08fqwRERkXHHjh1DSEhItfm6JK033ngDs2fPxubNm61a7tKiedSCICAmJgYxMTFWC4SIiExXXl6On3/+mQtNycjy5cvx+++/o1WrVggODq4xbqvRqmctWLCgztfffPNNiwIhIiLz/PbbbwgLC4OPj4/UoRBkVD1r+/bt1Z5rNBpcvnwZDg4OaN++PRM1EVEjEUURhw8fxqOPPsqBZTIgm+pZJ06cqPY4ffo0srKy8Le//Q3Tp083O4CVK1ca7rOEh4dj//79de5fXl6OOXPmIDg4GE5OTmjfvj02bNhg9nWJiJqC69ev4+rVq1KHQf8ji+pZxnh4eGDBggV47LHHMHr0aJOP27p1K6ZNm4aVK1ciOjoaa9aswZAhQ3D27Fm0adPG6DEjRozAX3/9hfXr16NDhw7IyckxWh+biKi5OHr0KIKCgtiqlpitqmdZJVEDlb8iCgoKzDpm6dKliIuLw/jx4wEASUlJ2LVrF1atWoXExMQa+ycnJyM1NRWXLl0yjKi7e+EVahilUgmlUmnVcwqCAEEQoFAooFAoDNdQKpVwcHCAg4PVPoZ2R19RAX1xEfQVFRA1GkAUrXp+0cH0/5Zr1qxBUVER3Nzc8OKLL1o1DrKtmzdv4sKFC7j33nulDqVZq6qetWTJEri7uxu2DxkyBCNHjrT4vGZ/Qy5fvrzac1EUkZWVhU2bNmHw4MEmn6eiogJpaWk11gePiYnBoUOHjB7zzTffICIiAkuWLMGmTZvg6uqKYcOG4a233oKzs7PRY8rLy1FeXm54XlRUZHKMzZFOp7PJPMC6CIIAR0dHODo6QqVSQaVSWf3HgtzoSkqgu3UT+ory+ndu0IVM/29ZVFRktKAA2Yfjx4+jffv2Tf7/HTmzVfUssxP1smXLqj1XKBTw8/PDc889h9mzZ5t8ntzcXOh0OgQEBFTbHhAQUOsbunTpEg4cOAC1Wo3t27cjNzcXkyZNQn5+fq33qRMTEzF//nyT46LGJ4oiKioqUFFRgeLiYgCViweo1Wo4OTlBpVI1mS49UauFJjcH+rIyqUOhJqaoqAgZGRno3Lmz1KE0W7aqnmV2or58+bLFFzPm7i9gURRr/VLW6/UQBAFbtmyBp6cngMru86effhorVqww2qqePXs24uPjDc9PnjyJvn37WvEdkC1otVoUFRWhqKgICoUCarUazs7OcHJykjo0i+mKi6HNvQFR1EsdCjVRJ06cwH333QdHR0epQ2mWqqpnffHFFwAkrJ51t8LCQuzYsQMZGRlmHefr6wulUlmj9ZyTk1OjlV0lMDAQrVu3NiRpAAgNDYUoirh27ZrRY5ycnODh4WF4uLm5mRUnSU+v16OkpAR5eXn466+/cPv27Ubvnm8o7a1b0Nz4i0mabKq0tBRnz56VOoxmy1bVs8xO1CNGjMCHH34IoPJDERERgREjRqBr1674+uuvTT6PSqVCeHg4UlJSqm1PSUlBVFSU0WOio6Nx/fr1aveZz58/D4VCgXvuucfct0J2SKfT4fbt28jJycHNmzdRUVEhdUj10ubnQXsrX+owqJn49ddfDfWQqXFVVc/6+uuvsXjxYrz88svYuXMnUlNTG1Rd0uxEvW/fPsOSddu3b4coirh16xaWL1+OhQsXmnWu+Ph4rFu3Dhs2bEBGRgamT5+OzMxMTJw4EUBlt/WYMWMM+48cORI+Pj4YN24czp49i3379mHmzJl4/vnnax1MRk2TKIooLS1Fbm4ucnNzUSbTe77am/nQFpo3G4KoIUpLS3H+/Hmpw2g2vL29kZubCwB4/vnncfv2bfTv3x8zZszArFmzGlziErAgURcUFBimRiUnJ+Opp56Ci4sLHn30UVy4cMGsc8XGxiIpKQkLFixA9+7dsW/fPuzcuRPBwcEAgKysLGRmZhr2d3NzQ0pKCm7duoWIiAiMGjUKQ4cOrTESnZqXiooK5Ofn48aNG9VG+EtNW1gAbcEtqcOgZujUqVMQrTzVj4yrqKgwDCD75JNPbNJoMHswWVBQEA4fPgxvb28kJyfj3//+N4DKeXyWVHGZNGkSJk2aZPS1jRs31th2//331+guJwIql7PNy8szjEuQckCNrqQE2nx2d5M0CgsLce3aNQQFBUkdSpMXGRmJJ554AuHh4RBFEVOnTq21h9fSVTTNTtTTpk3DqFGj4ObmhuDgYDz88MMAKrvEu3TpYlEQRNZUXl6OGzduwMXFBe7u7o0+r1TUaKC9kQOALRqSzoULF5ioG8HmzZuxbNkyXLx4EUBlr7O1W9VmJ+pJkyahV69eyMzMxMCBA6FQVPaet2vXzux71ES2VFJSgrKyMri5ucHV1bVR5mKLogjNjRyO7ibJXblyBXq93vAdTbYREBCAxYsXAwBCQkKwadMmq1czs2jtxvDwcISHh1fb9uijj1olICJr0uv1KCwsRGlpKby8vGzeHa67mW/71caITKDRaHDjxo1ap7uSdXh7e+P8+fPw9fVFv379oFKprH4N/tSiZkGj0SA3NxeFhYU2G2SjLy3lCG+SlYYsW2kr5lZMrHLw4EE4ODige/futg3QTLIcTEZkr0RRRFFREcrLy63euhb1emjycq12PiJruHXrltQhVGNJxUSg8r7vmDFj8Le//Q1//fVXI0ZcP1kOJiOyd1Wtazc3N7i5uVnl3rXu1k2IWi4yQfJibkVDWzO3YmKVF198ESNHjoRSqcSOHTusHleLFi1M/h7Iv2s2x52DyQRBkG4w2ZNPPomNGzfCw8MDn376KWJjY+16zWUiURRx+/ZtlJWVNbh1ra+ogJZVp0iGSktLpQ7BwJKKiQDw8ccf4+LFi9i8ebPNBiwnJSUZ/p2Xl4eFCxdi0KBBiIyMBAAcPnwYu3btwty5c2scK5vBZN999x2Ki4vh4eGBcePGYfDgwfD397dqIERSqBpw4+rqCnd3d4tGyGrzc8GpWCRHjZWo7y6R6uTkVKMxZ0nFxAsXLuD111/H/v37bVq3/rnnnjP8+6mnnsKCBQvw8ssvG7ZNnToVH374IX744QdMnz691vNYu2hVFZPe+f3334/Zs2ejX79+EEURX3zxBTw8PIzue+eSn0T2ori4GKWlpfDw8ICLi4vJx+mKi1iyksyWmZmJkpISAP9/Zb2qFR+tSavVWv2cxtxdkTAhIQHz5s0zuq+pFRN1Oh1GjhyJ+fPn495777VarPXZtWsX3nnnnRrbBw0aVKM3AACWL1+OF154AWq1ut5VMqdOnWpRTCYl6tWrVyM+Ph7//e9/IQgC3njjDaN/WEEQmKjJbun1ety6dcvQe1Tf7R1RFLn6GJnl2LFjeOutt/Df//7XMPugpKQE//znP9GlSxc8+uijaNu2rdWup9frG2UudWpqarXR2Mb+3zG3YuLt27dx/PhxnDhxwtC61ev1EEURDg4O2L17N/r372/dNwLAx8cH27dvx8yZM6tt37Fjh9Eu7WXLlmHUqFFQq9VYtmxZrecVBMG2iToqKgpHjhwBACgUCpw/f55d39Rk3bkUqbu7e63zInW3CyHqGqfF0hiqSsCyFKxtbNu2DbGxsRBFscYUQVEUcfr0aZw+fRoTJkzAAw88YLXrNsaa325ubrX2sla5s2Li8OHDDdtTUlLw+OOP19jfw8MDp06dqrZt5cqV2Lt3L7766iuEhIRYJ/i7zJ8/H3Fxcfjpp58M96iPHDmC5ORkrFu3rsb+d3Z3S9r1fafLly/Dz8/PFrEQyUp5eTnKy8vh5OQENze3aq0EUa+HTmZTX+pT3xf2Cy+8YPK+ZJ5jx44hNjYWOp2u1r+tXl+5mt1HH32E1157zaota7mIj4/H6NGjERERgcjISKxdu7ZGxcQ///wTn376KRQKBTp37lzteH9/f6jV6hrbrWns2LEIDQ3F8uXLsW3bNoiiiLCwMBw8eBC9evWy2XXrYnaiDg4Oxq1bt7B+/XpkZGRAEASEhoYiLi4Onp6etoiRSFJVCdvR0RGurq5wdnaGrrAAol7X4HMrVE5QuLhC4aSC4Gj9FY2cbt6EIAg2S7yCINhkJaamZuHChUZb0rXZuXNnrcWK7FlsbCzy8vKwYMECZGVloXPnznVWTJRKr169sGXLFpP2jY+PN/m8S5cutSgeQTTz/+Djx49j0KBBcHZ2Rs+ePSGKIo4fP47S0lLs3r3bql02tpCeno7w8HCkpaXJPta7ZWVlYe3atQBqDshoqKqPwfDhw+Hr62vVczc1AgDHWzehdlLBwcKCH0oXFyi9vKFohCSXl5eHiooKm5xbpVJZfSpKU5OZmYm2bdua9WNJEAS8/fbbVhlgFhcXZ7PCNPb8fVqXixcv4uOPP8alS5eQlJQEf39/JCcnIygoCJ06daq2b79+/ao9T0tLg06nw3333QcAOH/+PJRKJcLDw7F3716L4jG7RT19+nQMGzYMH330kWG4vFarxfjx4zFt2jTs27fPokCI7IX29m2Ul5agqLQEjg4OUKtUUDs5QWnCgB3BwRGOvn5QWFAS1lJMpJUjiKu6lhvbrl27zO7REEURZ8+eNdwjbQitVmuz995Yo8obU2pqKoYMGYLo6Gjs27cPCxcuhL+/P3799VesW7cOX331VbX9f/zxR8O/ly5dCnd3d3zyySdo0aIFgMoS0OPGjUPv3r0tjsnsFrWzszNOnDiB+++/v9r2s2fPIiIiwjDlQK7s+RcgW9TSE0UR2r+yjVbHclA6QK1yhMqx8nE3pasbHHx8IbCaUaObN28e5s+fL3UYTZY9fp/WJjIyEs888wzi4+Ph7u6OX375Be3atcPPP/+MJ554An/++Wetx7Zu3Rq7d++u0eo+ffo0YmJicP36dYtiMrtF7eHhgczMzBqJ+urVq3B3d7coCCJ7oS8qqrWEpVanRVGpFigtrbx36+AAx/891N4+cPTlIEypzJ07F3PmzJHk2hs3bqw2UM9Uo0ePtkqLOi4uzmYlXk+cOCHZACtbOXXqFD777LMa2/38/JCXl1fnsYWFhfjrr79qJOqcnBzcvn3b4pjMTtSxsbGIi4vDu+++i6ioKAiCgAMHDmDmzJn4xz/+YXEgRHIniiL0JUUm71uu0aBco4HSwxPFFRoosrPh4OAABwcHKBQKKJVKKBQKKBQKCIJgky9TQRCgz70Bsdw2i7IITmqoWgba5NzWpFQqbXaftj6DBg0ye0CfIAgICwtrcMy2Huxny9XCpOLl5YWsrKwa079OnDiB1q1b13ns8OHDMW7cOLz33nt46KGHAFRO7Zo5cyaefPJJi2My+6/87rvvGhY2qbo/4ejoiJdeesmw3ilRUySWlUI0816f0t0DStfKecl6vR4VFRU2G9hljOJ2IYoW27bLt03iUrtI1lJp06YNHnvsMezcuRM6Xf0zBRQKBbp06WKVgWRS/TixZyNHjsRrr72GL7/8svKHrl6PgwcPYsaMGfUu6LV69WrMmDEDzz77LDSayiI9Dg4OiIuLw7/+9S+LYzI7UatUKrz//vtITEzExYsXIYoiOnToYNayi0T2SFdkWmu6itLVDUo3aW8HiRXltr+GjVrrTcncuXPx/fffm9yyfuSRR6xy3abY4rW1RYsWYezYsWjdurVhDnXVcqZvvPFGnce6uLhg5cqV+Ne//lUtP7q6ujYoJov/K7q4uKBLly4NujiRvRDLy8wqY6lQq6H0kNm6AvV0rQ//ZhdulJbCz9kZ24cNqv98XBTFZA8++CC2bt1qWJnMWMu6apnPF154wWqLnXCOu3lEUcT169fx0Ucf4a233kJ6ejr0ej169OiBjh07mnweV1dXdO3a1Wpx8ecWkQm0xcUm7ys4OEDpZf0CC7Z2o7QUf5XIpyxiU/Pkk0/i0KFDeOutt/Ddd99Va1kLgoAuXbrgkUceseqKZOpGnAbYFIiiiI4dO+LMmTPo2LEj2rVrJ3VIAJioieolarVmdO8KcPDyttkoW7JvDz74IL755htkZmaie/fuuHnzJlxcXDB37lybVM/iLUnzKBQKdOzYEXl5eWa1oG2NEzqJ6qEvMb01rfTwgGBkDjXRndq0aWNIoiqVyiZJGgCXdbbAkiVLMHPmTJw+fVrqUAzYoiaqgyiK0BebtoiPoFIZRngTyYGXl5fUIdidZ599FiUlJejWrRtUKhWcnZ2rvZ4vQWlbixL1+fPn8dNPPyEnJ6fG0nRvvvmmVQIjkgOxtAQi6p+SJUCAg2eLRoiIyHSsdGi+pKSkBh1vi/xodqL+6KOP8NJLL8HX1xctW7asdi9OEAQmampSdCa2phUeHhA4FYZkxNHRkS1qCzz33HMWH2ur/Gj2N8vChQuxaNEivPbaaxZdkMheiBoNRG39i5MIDo5QuDRsniSRtQUGBhqmfFHdCgsL4eHhYfh3Xar2M8ZW+dHsRH3z5k0888wzVg2CSI50Jg4ic/D04ihvkp1WrVpJHYLdaNGiBbKysuDv7w8vL+P/P4uiCEEQ6lxdzlb50exE/cwzz2D37t2YOHGi1YMhkgtRFCGW1j+nWKF2gcBFJUiGgoODpQ7Bbuzdu9cw8v7OspXmslV+NDtRd+jQAXPnzsWRI0fQpUsXON41FWXq1KlWC45IKmJZWa1VsqoIEKCsoxuMSCotWrTg1Cwz9O3b1+i/zWWr/Gh2ol67di3c3NyQmpqK1NTUaq8JgsBETU2CKd3eCldXCCx6QDLUvn17qUOweyUlJcjMzKxRRKeupUFtlR/NTtSXL1+26EJE9kLU6SDWU+FKgAIKiQtuEBkjCIKsVtWyNzdu3MC4cePw/fffG329rnvUtsqPDRoSKIqiWTVWieyBvrQEQN2fa4WbGwSOqCUZuueee+Duzh+Rlpo2bRpu3ryJI0eOwNnZGcnJyfjkk0/QsWNHfPPNNyafx5r50aJvmk8//RRdunSBs7MznJ2d0bVrV2zatMkqARFJTV/PILLK1jRXICN56ty5s9Qh2LW9e/di2bJlePDBB6FQKBAcHIxnn30WS5YsQWJiYr3H2yI/mt31vXTpUsydOxcvv/wyoqOjIYoiDh48iIkTJyI3NxfTp09vUEBEUqqcO113OUuFmxunY5Es+fn54Z577pE6DLtWXFwMf39/AIC3tzdu3LiBe++9F126dEF6enqdx9oqP5qdqD/44AOsWrUKY8aMMWx7/PHH0alTJ8ybN4+JmuyavoytabJfDzzwAH9ENtB9992Hc+fOoW3btujevTvWrFmDtm3bYvXq1QgMDKzzWFvlR7MTdVZWFqKiompsj4qKQlZWlkVBEMmFvqTuJUMVbq78IiRZatWqFdq0aSN1GHZv2rRphlyWkJCAQYMGYcuWLVCpVNi4cWOdx9oqP5p9j7pDhw744osvamzfunUrRxqSXRM1FRD1tY/oBIQmvVSon7MzAlyc4XdXtSCSP0EQEBkZyR+RDVBSUoLJkydj1qxZmDVrFkaOHImgoCD88ccf+Pnnn3H16lXExsbWeQ5b5UezW9Tz589HbGws9u3bh+joaAiCgAMHDmDPnj1GAySyF7r6WtPOzk163vT2YYOkDoEs1LlzZ/j4+Egdhl1LSEjAxo0bMWrUKDg7O+Ozzz7DSy+9hC+//BIPPPCASeewVX40O1E/9dRTOHr0KJYtW4YdO3ZAFEWEhYXh2LFj6NGjh8WBEElNrOf+NGtNkxy5uroiIiJC6jDs3rZt27B+/Xr8/e9/BwCMGjUK0dHR0Ol0UJr4A91W+dGiunzh4eHYvHmzxRclkhuxvByivvYlQwUnNYS7lgMkkoOoqKgaS1WS+a5evYrevXsbnvfs2RMODg64fv06goKCTD6PLfKjSYnaWiXAiORKV09r2sG16d6bJvsVFBSEtm3bSh1Gk6DT6aC6q8COg4MDtFptncc1Rn40KVFbqwQYkVzV1e0tODhAcFI3YjRE9VMoFIiKiuIAMisRRRFjx46Fk5OTYVtZWRkmTpwI1zt+qG/btq3acY2RH01K1NYqAUYkR2JFRZ3d3k15pDfZr7CwMFbIsqLnnnuuxrZnn3223uMaIz+alKjvLPsVEhKCoKCgGr8aRFHE1atXrRsdUSOoa5EToYlPySL75OjoaPJIZDLNxx9/bNFxjZEfzZ5HHRISghs3btTYnp+fj5CQELMDWLlyJUJCQqBWqxEeHo79+/ebdNzBgwfh4OCA7t27m31NojvVtba34OzMrkWSnS5dukCt5u0YubF2fqxidqKu6mu/W1FRkdkfnK1bt2LatGmYM2cOTpw4gd69e2PIkCHIzMys87iCggKMGTMGf/vb38y6HtHdRI2mzkVOOCWL5EalUqFLly5Sh0FGWDM/3snk6Vnx8fEAKlfAmTt3LlxcXAyv6XQ6HD161OzW7dKlSxEXF4fx48cDAJKSkrBr1y6sWrWqziolL774IkaOHAmlUokdO3aYdU2iO9XZ7e2g4pQskp1OnTpVG/BE0rNFfryTyYn6xIkTACp/MZw6daraMHaVSoVu3bphxowZJl+4oqICaWlpeP3116ttj4mJwaFDh2o97uOPP8bFixexefNmLFy4sN7rlJeXo7y83PC8qKjI5Bip6dOXldX6msKFS2mSvDg6OrI1LUPWzo93MzlRV41mGzt2LD744IMGFybPzc2FTqdDQEBAte0BAQHIzs42esyFCxfw+uuvY//+/XBwMC30xMREzJ8/v0GxUtMkarW1lrTkIDKSo06dOvHetAxZOz/ezax71FqtFps3b8aVK1esFoCx0XHG+vh1Oh1GjhyJ+fPn49577zX5/LNnz0ZBQYHhkZqa2uCYqWnQl9femhbUHERG8sLWtLzZIj9WMWsJUQcHBwQHB1tlURNfX18olcoareecnJwarWwAuH37No4fP44TJ07g5ZdfBgDo9XqIoggHBwfs3r0b/fv3r3Gck5NTtfs5bqwlTP9T1/1pJbu9SWY6deoEZ1Y2ky1r5se7mT3q+4033sDs2bORn5/foAurVCqEh4cjJSWl2vaUlBSj9Tw9PDxw6tQpnDx50vCYOHEi7rvvPpw8eRK9evVqUDzUvIiiCLGilm5vhYIrkZGsVN3nJHmzVn68m9lFOZYvX47ff/8drVq1QnBwcLWl1QAgPT3d5HPFx8dj9OjRiIiIQGRkJNauXYvMzExMnDgRQGW39Z9//olPP/0UCoUCnTt3rna8v78/1Gp1je1E9RHLygCIRl9TOLsY3U4kla5du3Kktx2wZn68k9mJ+oknnrDoQsbExsYiLy8PCxYsQFZWFjp37oydO3ciODgYAJCVlVXvnGoiS9Q52puJmmRErVbz3rSdsGZ+vJPZiTohIcGqAUyaNAmTJk0y+trGjRvrPHbevHmYN2+eVeOh5kGsZSAZ506T3HTr1o1lLO2EtfNjFYvqUQNAWloaMjIyIAgCwsLCGlQUm6gxiRUVEEXjRTgUzrw3TfKhVqsRFhYmdRhkJmvnR7MTdU5ODv7+97/jp59+gpeXF0RRREFBAfr164d///vf8PPza1BARLZW17QsdnuTnHTp0oWtaTtiq/xo9qjvKVOmoLCwEGfOnEF+fj5u3ryJ06dPo7CwEFOnTrUoCKLGVNv9acFRBUGpbORoiIxzdHRka9rO2Co/mp2ok5OTsWrVKoSGhhq2hYWFYcWKFfj+++8tDoSoMYg6Xa2rkSk4R5VkpGPHjk1ypLc5FRO3bduGgQMHws/PDx4eHoiMjMSuXbsaMVrz2Co/mp2o9Xq90a4YR0dH6PXG7/sRyYVY62hvAQo1EzXJR1NsTZtbMXHfvn0YOHAgdu7cibS0NPTr1w9Dhw41rK0tN7bKj2Yn6v79++OVV17B9evXDdv+/PNPTJ8+nWUnSfZ0taxGJqgc2e1NsuHv7w9vb2+pw7C6OysmhoaGIikpCUFBQVi1apXR/ZOSkjBr1iw8+OCD6NixI95++2107NgR3377bSNHbhpb5UezE/WHH36I27dvo23btmjfvj06dOiAkJAQ3L59Gx988IHFgRDZWuVqZBVGX1OqOYiM5KNjx45Sh2B1VRUTY2Jiqm2vr2LinfR6PW7fvi3bHzG2yo9mj/oOCgpCeno6UlJS8Ntvv0EURYSFhWHAgAEWB0HUGOpajUxgRSKSCUEQEBISInUYZikqKkJhYaHh+d01FgDLKibe7b333kNxcTFGjBjR8KBtwFb50eJ51AMHDsTAgQMbdHGixsTR3mQP/P394eJiXz08ffv2rfY8ISGh1sWoTK2YeLfPP/8c8+bNw3/+8x/4+/tbHGtjsHZ+NLvrGwD27NmDxx57zNC0f+yxx/DDDz9YLSgiaxNFsdaBZBztTXLSpk0bqUMwW2pqarVywrNnz66xj7kVE++0detWxMXF4YsvvpB9760t8qNF96gHDx4Md3d3vPLKK5g6dSo8PDzwyCOP4MMPP2xQMES2IpaXQ0Qtq5FxtDfJSOvWraUOwWxubm7w8PAwPIxNKzO3YmKVzz//HGPHjsVnn32GRx991OqxW5Ot8qPZXd+JiYlYtmyZoSY0AEydOhXR0dFYtGhRte1EclFb7WnBgd3eJB8qlapJr+5oTsVEoDJJjxkzBu+//z4eeughQ2vc2dkZnp6ekr2P2tgqP5rdoi4sLMTgwYNrbI+Jiak2mIBILurs9uYgMpKRli1bmnS/1l7FxsYiKSkJCxYsQPfu3bFv3746KyauWbMGWq0WkydPRmBgoOHxyiuvSPUW6mSr/Gh2oh42bBi2b99eY/t//vMfDB061OJAiGymriIcTNQkIy1btpQ6BJubNGkS/vjjD5SXlyMtLQ19+vQxvLZx40b89NNPhuc//fRT5Q/tux71VVaUiq3yo9ld36GhoVi0aBF++uknREZGAgCOHDmCgwcP4tVXX8Xy5csN+3Ltb5KDWhc5UTqwpCXJSn2DqkjebJUfzU7U69evR4sWLXD27FmcPXvWsN3Lywvr1683PBcEgYmaZEGsJVGzNU1yIghCk74/3RzYKj+anagvX75s7iFEkhHLyyHWssYuR3uTnPj4+MDBweKlLUgGbJUfLZpHXaXqfgGRXOlKS4xuFwQFwG5vkhG2ppsWa+ZHixL1p59+ii5dusDZ2RnOzs7o2rUrNm3aZJWAiKxFFEWIpbWsRuakbtKja8n+MFE3DbbIj2b3syxduhRz587Fyy+/jOjoaIiiiIMHD2LixInIzc3F9OnTGxQQkbXUvcgJ70+TvPj6+kodAjWQrfKj2Yn6gw8+wKpVqzBmzBjDtscffxydOnXCvHnzmKhJNvSlxgeRAQKLcJCsKBQKtGjRQuowqIFslR/N7vrOysoyutxbVFQUsrKyLAqCyNoqFzmpZVqWoyO7vUlWPD09oeQKeXbPVvnR7ETdoUMHfPHFFzW2b926tUnWUCX7JJaVQqylpKXCia1pkhcvLy+pQyArsFV+NLvre/78+YiNjcW+ffsQHR0NQRBw4MAB7Nmzx2iARFLQlRgf7Q3w/jTJQ8uWLaHX6+Ho6AgPDw+pwyErsFV+NDtRP/XUUzh27BiWLl2KHTt2GApjHzt2DD169LA4ECJrEXU6iBXlRl8TFEquRkaycPz4cdy4cQPbt29nom4ibJUfzUrUGo0GL7zwAubOnYvNmzdbfFEiW9LX0ZrmIDKSIzc3N6lDoAayZX406x61o6Oj0QXHieREX1Z7olZyNTKSIRcXF6lDoAayZX40ezDZ8OHDsWPHDhuEQtRwYkUFRK3W6GsCFIBK1cgREdWPibppsFV+NPsedYcOHfDWW2/h0KFDCA8Ph6ura7XXWYiDpKQrLq71NUHN1chIfgRBgJq3ZJoEW+VHsxP1unXr4OXlhbS0NKSlpVV7jRWzSEp1zZ0GONqb5EnNH5BNhq3yI6tnUZMhlpbUOneaq5GRXLE13XSwehZRPXTFdYz2dnJiq4VkycnJSeoQyAYkr561fv16dO7cGWq1Gmq1Gp07d8a6deusEhCRJURNBURtRa2vK5052pvkiYm6abFFfjS763vu3LlYtmwZpkyZgsjISADA4cOHMX36dPzxxx9YuHBhgwIisoSuqPZBZIAAgcuGkkwxUTcdtsqPZifqVatW4aOPPsI//vEPw7Zhw4aha9eumDJlChM1NTpRr4e+jkFkgkoFQdGguzxENsNE3XTYKj+a/e2l0+kQERFRY3t4eDi0tcxfJbIlfXExUOsgMi5yQvLGRN102Co/mp2on332WaxatarG9rVr12LUqFEWB0JkCVEUoS8pqmMPAQLvT5OMMVE3HbbKj2Z3fQOVN8t3796Nhx56CABw5MgRXL16FWPGjEF8fLxhv6VLl1ocGJEpxLJSiHp9ra+z25vkTsXV8poUW+RHsxP16dOn8cADDwAALl68CADw8/ODn58fTp8+bdiPU2GoMeiK6mpNc7Q3yZ8jq7k1GbbKj2Yn6h9//NHcQ4hsQiwvg6jV1LGHAMGZayiTvDFRNx22yo/sEyS7pS26XefrCjUXOSH5c3Cw6A4kNSNM1GSXxIoKiBW1L3ACAAo1W9Mkf0zUVB8marJL9bWmBa7tTXaCiZrqw0RNdkfUaCCWl9W5j6B2Zrc32QWlUil1CCRzTNRkd7SFBfXuo3RhtzfZByZqqg8TNdkVsbwcYkV5nfsICiUELiJBdkLBef5UD8k/IStXrkRISAjUajXCw8Oxf//+Wvfdtm0bBg4cCD8/P3h4eCAyMhK7du1qxGhJatrCwnr3UbA1TXaEiZrqI+knZOvWrZg2bRrmzJmDEydOoHfv3hgyZAgyMzON7r9v3z4MHDgQO3fuRFpaGvr164ehQ4fixIkTjRw5SUFfWlpnKcsqCq7tTXaEiZrqI+lww6VLlyIuLg7jx48HACQlJWHXrl1YtWoVEhMTa+yflJRU7fnbb7+N//znP/j222/Ro0ePxgjZbqxZswZFRUVwc3PDiy++KHU4DSaKInS3629NC44qCFxAguwIBz1SfST7KVdRUYG0tDTExMRU2x4TE4NDhw6ZdA69Xo/bt2/D29vbFiHataKiIhQWFqKoniU27YW+qAiirv7qM0pX10aIhsh6mKipPpK1qHNzc6HT6RAQEFBte0BAALKzs006x3vvvYfi4mKMGDGi1n3Ky8tRXv7/Bx81lcTVnIg6HfT1zJsGAAEKCOz2JjsiCAITNdVL8psjd39IRVE06YP7+eefY968edi6dSv8/f1r3S8xMRGenp6GR9++fRscMzUuXeEtiHXUm64iuHDuNNkX3p8mU0j2KfH19YVSqazRes7JyanRyr7b1q1bERcXhy+++AIDBgyoc9/Zs2ejoKDA8EhNTW1w7NR49KWl0JfVvbhJFaULu73JvvCHJZlCskStUqkQHh6OlJSUattTUlIQFRVV63Gff/45xo4di88++wyPPvpovddxcnKCh4eH4eHm5tbg2KlxiHo9dAW3TNqXg8jIHrFFTaaQdNR3fHw8Ro8ejYiICERGRmLt2rXIzMzExIkTAVS2hv/88098+umnACqT9JgxY/D+++/joYceMrTGnZ2d4enpKdn7INvQ3roJUdSbtK/SlT/AyP6wRU2mkDRRx8bGIi8vDwsWLEBWVhY6d+6MnTt3Ijg4GACQlZVVbU71mjVroNVqMXnyZEyePNmw/bnnnsPGjRsbO3yyIV1xUb3reVcRBAULcJBdYouaTCF52ZZJkyZh0qRJRl+7O/n+9NNPtg+IJCdqKqAzYQWyKgpXV7ZMyC7xc0um4M85khVRp4M2Pw8wYZR3JQEKDiIjO8UWNZmCnxKSDVEUoc3Ph6g37b40ULlcqMDqQ2Sn2KImU0je9U2WEcW6W5xVr4uiWO++d7LFAgymXL8ySeeatJb3nZQcxU9ETRwTtR1RqVQQBMGkxFc1Dc2c6WiCIKBVq1bw8fGxOMbaiKIIvV4PvV4PnU5neGi1WmgqKlB2IwdihXlJWlA5cUoW2bXm2KJeuXIl/vWvfyErKwudOnVCUlISevfuXev+qampiI+Px5kzZ9CqVSvMmjXLMDOouWCitiM+Pj6YPHkyKkxIaC+88ILZ51epVDZJ0kDlF5JSqYRSqYTjHclV1GqhyfkLWlcXaJxU0Gi1qNBoodFq612NzMHN3SaxEjWW5paoqyomrly5EtHR0VizZg2GDBmCs2fPok2bNjX2v3z5Mh555BFMmDABmzdvxsGDBzFp0iT4+fnhqaeekuAdSIOJ2s7YKpFKQV9WBs2NHIg6LRQKBZxUKjipVAAqW+AVGg3KKzQo11RAd9d9a8FRBcHJSYqwiaymuSVqcysmrl69Gm3atDFUTgwNDcXx48fx7rvvNqtEzcFk1OhEvR7a/DxUZF+vtSKWIAhwUqng4eYKvxYt4O3hARe1Gor/fbE5uLM1TWRPLKmYePjw4Rr7Dxo0CMePH4dGo7FZrHLDFjU1GlGng+72begKCyDqdWYdq3J0hMrREe4uLqiAAK2HZ7WqaER10el00Jsxm6Cx2Huy0Worf2hXldWt4uTkBKe7erwsqZiYnZ1tdH+tVovc3FwEBgZa423IHhO1nanIzjJ5xS5ziSLg6Odn9ZOKGg305WXQl5XD9PnRxgmCAPeAllA4u0Cr1aKoqAilpaVmjWyn5uett97C/PnzpQ6jybq7KmFCQgLmzZtndF9zKyYa29/Y9qaMidqOVGRnIXN2vO0uIIpo+XI8HHx9bXeNBlKo1VA4uwAAHBwc4OXlBXd3dxQXF6O4uJgJm4yaO3cu5syZI3UYTc6JEyfQq1cvpKamonv37obtd7emAcsqJrZs2dLo/g4ODk1qvE59mKjtiK1a0tWuoTFvilRjc2jhXWObUqk0VEYrKipiwqYaqmYckHU5OFSmEDc3N3h4eNS5750VE4cPH27YnpKSgscff9zoMZGRkfj222+rbdu9ezciIiKqzR5p6jiYzF4JgnUfdkDp4gqFU+3FNxQKBTw8PODv7w9nZ+dGjIyITBEfH49169Zhw4YNyMjIwPTp02tUTBwzZoxh/4kTJ+LKlSuIj49HRkYGNmzYgPXr12PGjBlSvQVJsEVNdkEQBDh4m9bVpVQq0aJFC7i6uqKwsNCkeedEZHvmVkwMCQnBzp07MX36dKxYsQKtWrXC8uXLm9XULICJuska/s0u3CgthZ+zM7YPGyR1OA2m9PCE4GDex1WlUsHX1xclJSUoLCyU5ahfoubGnIqJQOVAtfT0dBtHJW9M1E3UjdJS/FVSKnUYViEoHaD09LL4eBcXF6jVahQUFKC0tGn8TYio+eA9apI9Bx9fCA0sB6hQKNCiRQt4e3tzUBER2RUmapI1pasblC4uVjufWq2Gn58f1OraB6UREckJEzXJlqBQmjyAzBwKhQLe3t5o0aIFFA1sqRMR2Rq/pUi2HP38INiwm9rZ2ZlTuYhI9pioSZYcPDwNK5DZUtW9ax8fH8PiDUREcsJETbKjUDlBaWQFMltycnKCn58fPDw82B1ORLLCbySSFUHpAMeAlpIsuC8IAtzc3ODv7w9XV9dmteg/EckXEzXJhiAoKpO0xNOnFAoFPD094e/vDxcXFyZsIpIUEzXJgiAIcAxoCYVKJXUoBkqlEl5eXmxhE5GkOHqGJCcIAhz9W0Ih07nNSqUSnp6ecHd3R0lJCYqLi6HT6aQOi4iaCSbqJsrvf1OO/GQ+9UhQOsDRPwAKI/Vr5UahUMDNzQ1ubm4oKytDSUkJysvLWVKTiGyKibqJsodCHAq1Go6+/mYX25ADtVoNtVoNvV6PsrIylJaWoqKigkmbiKzO/r4hye4JggLKFi3g4OEpdSgNplAo4OLiAhcXF+j1epSXlxse7B4nImtgoqZGIyiVULq5Q+np1eAiG3KkUCjg7OxsWOlMp9OhoqICGo3G8GCpTSIyFxM1VaNwdobS1c16JxQECI6OUDipZTtYzFaUSmW1xA0Aer0eOp3O8NDr9dDr9YYuc2t3nYuOjii26hmJqLExUVM1+tJS6IqLpA6jWVD+72FLmpv5Nr4CEdla0+t/JCIiakKYqImIiGSMiZqIiEjGmKiJiIhkjImaiIhIxpioiYiIZIyJmoiISMaYqImIiGSMiZqIiEjGmKiJiIhkjImaiIhIxpioiYiIZIyJmoiISMaYqImIiGSMiZqIiEjGJE/UK1euREhICNRqNcLDw7F///46909NTUV4eDjUajXatWuH1atXN1KkREREjU/SRL1161ZMmzYNc+bMwYkTJ9C7d28MGTIEmZmZRve/fPkyHnnkEfTu3RsnTpzAP//5T0ydOhVff/11I0dORETUOCRN1EuXLkVcXBzGjx+P0NBQJCUlISgoCKtWrTK6/+rVq9GmTRskJSUhNDQU48ePx/PPP4933323kSMnIiJqHJIl6oqKCqSlpSEmJqba9piYGBw6dMjoMYcPH66x/6BBg3D8+HFoNBqbxUpERCQVB6kunJubC51Oh4CAgGrbAwICkJ2dbfSY7Oxso/trtVrk5uYiMDCwxjHl5eUoLy83PC8qKrJC9DIgilJHQPaGnxkiuyRZoq4iCEK156Io1thW3/7GtldJTEzE/PnzGxilPAhOahtfQIBTSHuoWtb8wUP2SaF2tvk1bP65JGrmJEvUvr6+UCqVNVrPOTk5NVrNVVq2bGl0fwcHB/j4+Bg9Zvbs2YiPjzc8P3nyJPr27dvA6KWhahmINolLIZaX2eT8gpOaSbqJ4WeGyP5JlqhVKhXCw8ORkpKC4cOHG7anpKTg8ccfN3pMZGQkvv3222rbdu/ejYiICDg6Oho9xsnJCU5OTobnbm5uVoheOvxSJHPxM0Nk3yQd9R0fH49169Zhw4YNyMjIwPTp05GZmYmJEycCqGwNjxkzxrD/xIkTceXKFcTHxyMjIwMbNmzA+vXrMWPGDKneAhERkU1Jeo86NjYWeXl5WLBgAbKystC5c2fs3LkTwcHBAICsrKxqc6pDQkKwc+dOTJ8+HStWrECrVq2wfPlyPPXUU1K9BSIiIpsSRLF5DQVNT09HeHg40tLS8MADD0gdDhGR3eL3aeOQfAlRIiIiqh0TNRERkYwxURMREckYEzUREcnOzZs3MXr0aHh6esLT0xOjR4/GrVu3at1fo9HgtddeQ5cuXeDq6opWrVphzJgxuH79euMFbSNM1EREJDsjR47EyZMnkZycjOTkZJw8eRKjR4+udf+SkhKkp6dj7ty5SE9Px7Zt23D+/HkMGzasEaO2DcmXECUiIrpTRkYGkpOTceTIEfTq1QsA8NFHHyEyMhLnzp3DfffdV+MYT09PpKSkVNv2wQcfoGfPnsjMzESbNm0aJXZbYIuaiIhk5fDhw/D09DQkaQB46KGH4OnpWWt1RWMKCgogCAK8vLxsEGXjabYt6oyMDKlDICIrCgwMNFpBz1RZWVnIysqyYkRNX9X3aFFREQoLCw3b71662VzZ2dnw9/evsd3f37/W6op3Kysrw+uvv46RI0fCw8PD4lhkQWxmrl+/Lvbt21cEwAcffDShR0JCQoO+GxISEiR/D/b4CA4ONvm/hSl/459//llctGiReO+999Y4vkOHDmJiYmK9/y0rKirExx9/XOzRo4dYUFDQoM+FHDS7lcmApvvLuaioCH379kVqaqrdFx8hadnjZ8neWtT2+Dc2xtvbG97e3tW21daizs3NRW5ubp3na9u2LT777DPEx8fXGOXt5eWFZcuWYdy4cbUer9FoMGLECFy6dAl79+6ttbKiPWmWibqpKiwshKenJwoKCuy/q4ckxc+S7fFvXLuMjAyEhYXh6NGj6NmzJwDg6NGjeOihh/Dbb78ZHUwG/P8kfeHCBfz444/w8/NrzLBthoPJiIhIVkJDQzF48GBMmDABR44cwZEjRzBhwgQ89thj1ZL0/fffj+3btwMAtFotnn76aRw/fhxbtmyBTqdDdnY2srOzUVFRIdVbsQomaiIikp0tW7agS5cuiImJQUxMDLp27YpNmzZV2+fcuXMoKCgAAFy7dg3ffPMNrl27hu7duxtuhQQGBpo1UlyOmu2o76bIyckJCQkJDRptSQTws9QY+Deum7e3NzZv3lznPnfeuW3bti2a6p1c3qMmIiKSMXZ9ExERyRgTNRERkYwxURMREckYEzVV89NPP0EQhDrLyRERUeNhorah7OxsTJkyBe3atYOTkxOCgoIwdOhQ7Nmzx6rXefjhhzFt2jSrnrMua9euxcMPPwwPDw8mdRkRBKHOx9ixYy0+d9u2bZGUlFTvfk39s8G/MUmB07Ns5I8//kB0dDS8vLywZMkSdO3aFRqNBrt27cLkyZPx22+/NWo8oihCp9PBwaHh/8lLSkowePBgDB48GLNnz7ZCdGQNdy5/uXXrVrz55ps4d+6cYZuzs7PNY2jqnw3+jUkSkq0y3sQNGTJEbN26tVhUVFTjtZs3bxr+feXKFXHYsGGiq6ur6O7uLj7zzDNidna24fWEhASxW7du4qeffioGBweLHh4eYmxsrFhYWCiKoig+99xzNRa1v3z5svjjjz+KAMTk5GQxPDxcdHR0FPfu3SuWlZWJU6ZMEf38/EQnJycxOjpaPHbsmOF6VcfdGWNtzNmXGtfHH38senp6Vtv2zTffiA888IDo5OQkhoSEiPPmzRM1Go3h9YSEBDEoKEhUqVRiYGCgOGXKFFEURaNFbOrTHD4b/BtTY2HXtw3k5+cjOTkZkydPhqura43Xq2qjiqKIJ554Avn5+UhNTUVKSgouXryI2NjYavtfvHgRO3bswHfffYfvvvsOqampWLx4MQDg/fffR2RkJCZMmGAoKhAUFGQ4dtasWUhMTERGRga6du2KWbNm4euvv8Ynn3yC9PR0dOjQAYMGDUJ+fr7t/iAkuV27duHZZ5/F1KlTcfbsWaxZswYbN27EokWLAABfffUVli1bhjVr1uDChQvYsWMHunTpAgDYtm0b7rnnHixYsKDJFrSxBv6NyWak/qXQFB09elQEIG7btq3O/Xbv3i0qlUoxMzPTsO3MmTMiAEMrNyEhQXRxcTG0oEVRFGfOnCn26tXL8Lxv377iK6+8Uu3cVb+2d+zYYdhWVFQkOjo6ilu2bDFsq6ioEFu1aiUuWbKk2nFsUdu3u1t7vXv3Ft9+++1q+2zatEkMDAwURVEU33vvPfHee+8VKyoqjJ4vODhYXLZsmcnXbw6fDf6NqbGwRW0D4v8WexMEoc79MjIyEBQUVK0FHBYWBi8vL0NBdqBykIm7u7vheWBgIHJyckyKJSIiwvDvixcvQqPRIDo62rDN0dERPXv2rHY9anrS0tKwYMECuLm5GR5VvTAlJSV45plnUFpainbt2mHChAnYvn07tFqt1GHbFf6NyVaYqG2gY8eOEASh3uQniqLRZH73dkdHx2qvC4IAvV5vUix3dr3X9gOitjio6dDr9Zg/fz5OnjxpeJw6dQoXLlyAWq1GUFAQzp07hxUrVsDZ2RmTJk1Cnz59oNFopA7dbvBvTLbCRG0D3t7eGDRoEFasWIHi4uIar1dNpwgLC0NmZiauXr1qeO3s2bMoKChAaGioyddTqVTQ6XT17tehQweoVCocOHDAsE2j0eD48eNmXY/szwMPPIBz586hQ4cONR4KReXXgLOzM4YNG4bly5fjp59+wuHDh3Hq1CkApn/GmjP+jclWOD3LRlauXImoqCj07NkTCxYsQNeuXaHVapGSkoJVq1YhIyMDAwYMQNeuXTFq1CgkJSVBq9Vi0qRJ6Nu3b7Uu6/q0bdsWR48exR9//AE3Nzd4e3sb3c/V1RUvvfQSZs6cCW9vb7Rp0wZLlixBSUkJ4uLiTL5eVY3X33//HQBw6tQpuLu7o02bNrVem6T15ptv4rHHHkNQUBCeeeYZKBQK/Prrrzh16hQWLlyIjRs3QqfToVevXnBxccGmTZvg7OyM4OBgAJWfsX379uHvf/87nJyc4Ovra/Q6zfmzwb8x2Yykd8ibuOvXr4uTJ08Wg4ODRZVKJbZu3VocNmyY+OOPPxr2MXV61p2WLVsmBgcHG56fO3dOfOihh0RnZ+ca07PuHmhSWloqTpkyRfT19bV4elZCQkKNqSQAxI8//tiCvxLZgrGpQ8nJyWJUVJTo7Owsenh4iD179hTXrl0riqIobt++XezVq5fo4eEhurq6ig899JD4ww8/GI49fPiw2LVrV9HJyanOqUPN6bPBvzE1Fpa5JCIikjHeoyYiIpIxJmoiIiIZY6ImIiKSMSZqIiIiGWOiJiKyIdZ4p4ZiopbA2LFjIQiCobBGlR07djTqCmEvvvgiBEGoUQO3vLwcU6ZMga+vL1xdXTFs2DBcu3at0eIi0/GzJH9RUVHIysqCp6en1KGQnWKiloharcY777yDmzdvSnL9HTt24OjRo2jVqlWN16ZNm4bt27fj3//+Nw4cOICioiI89thjXDVJpvhZkjeVSoWWLVtymV6yGBO1RAYMGICWLVsiMTGx0a/9559/4uWXX8aWLVtqrCNeUFCA9evX47333sOAAQPQo0cPbN68GadOncIPP/zQ6LFS/fhZalwPP/wwpkyZgmnTpqFFixYICAjA2rVrUVxcjHHjxsHd3R3t27fH999/D6Bm1/fGjRvh5eWFXbt2ITQ0FG5ubhg8eHC10pYPP/wwpk2bVu26TzzxBMaOHWt4vnLlSnTs2BFqtRoBAQF4+umnbf3WSSJM1BJRKpV4++238cEHH5jVFThkyJBq1XmMPeqi1+sxevRozJw5E506darxelpaGjQaDWJiYgzbWrVqhc6dO+PQoUOmv0FqNPwsNb5PPvkEvr6+OHbsGKZMmYKXXnoJzzzzDKKiopCeno5BgwZh9OjRKCkpMXp8SUkJ3n33XWzatAn79u1DZmYmZsyYYfL1jx8/jqlTp2LBggU4d+4ckpOT0adPH2u9PZIZrvUtoeHDh6N79+5ISEjA+vXrTTpm3bp1KC0ttfia77zzDhwcHDB16lSjr2dnZ0OlUqFFixbVtgcEBCA7O9vi65Jt8bPUuLp164Y33ngDADB79mwsXrwYvr6+mDBhAoDKdb9XrVqFX3/91ejxGo0Gq1evRvv27QEAL7/8MhYsWGDy9TMzM+Hq6orHHnsM7u7uCA4ORo8ePRr4rkiumKgl9s4776B///549dVXTdq/devWFl8rLS0N77//PtLT082+XyayFKbs8bPUeLp27Wr4t1KphI+PD7p06WLYFhAQAADIycmBh4dHjeNdXFwMSRowr8Y8AAwcOBDBwcFo164dBg8ejMGDB2P48OFwcXGx5O2QzLHrW2J9+vTBoEGD8M9//tOk/RvSXbl//37k5OSgTZs2cHBwgIODA65cuYJXX30Vbdu2BQC0bNkSFRUVNQYm5eTkGL58SJ74WWo8xmrE37mt6odIbXXjjR1/Z9kFhUKBu8sw3Fm32t3dHenp6fj8888RGBiIN998E926deMUsCaKLWoZWLx4Mbp3745777233n0b0l05evRoDBgwoNq2qntp48aNAwCEh4fD0dERKSkpGDFiBAAgKysLp0+fxpIlSyy6LjUefpaaBj8/v2qDy3Q6HU6fPo1+/foZtjk4OGDAgAEYMGAAEhIS4OXlhb179+LJJ5+UImSyISZqGejSpQtGjRqFDz74oN59G9Jd6ePjAx8fn2rbHB0d0bJlS9x3330AAE9PT8TFxeHVV1+Fj48PvL29MWPGDHTp0qXGFzPJDz9LTUP//v0RHx+P//73v2jfvj2WLVtWrbX83Xff4dKlS+jTpw9atGiBnTt3Qq/XG/721LSw61sm3nrrrRpdXVJZtmwZnnjiCYwYMQLR0dFwcXHBt99+C6VSKXVoZAJ+luzf888/j+eeew5jxoxB3759ERISUq017eXlhW3btqF///4IDQ3F6tWr8fnnnxsdfU/2j/WoiYiIZIwtaiIiIhljoiYiIpIxJmoiIiIZY6ImIiKSMSZqIiI7xDrXzQcTNRE1e9nZ2ZgyZQratWsHJycnBAUFYejQodizZ49Vr2OsKpYtrV27Fg8//DA8PDyY1O0YEzURNWt//PEHwsPDsXfvXixZsgSnTp1CcnIy+vXrh8mTJzd6PKIoQqvVWuVcJSUlGDx4sMnLypJMiUREzdiQIUPE1q1bi0VFRTVeu3nzpuHfV65cEYcNGya6urqK7u7u4jPPPCNmZ2cbXk9ISBC7desmfvrpp2JwcLDo4eEhxsbGioWFhaIoiuJzzz0nAqj2uHz5svjjjz+KAMTk5GQxPDxcdHR0FPfu3SuWlZWJU6ZMEf38/EQnJycxOjpaPHbsmOF6VcfdGWNtzNmX5IctaiJqtvLz85GcnIzJkyfD1dW1xuteXl4AKlu5TzzxBPLz85GamoqUlBRcvHgRsbGx1fa/ePEiduzYge+++w7fffcdUlNTsXjxYgDA+++/j8jISEyYMAFZWVnIyspCUFCQ4dhZs2YhMTERGRkZ6Nq1K2bNmoWvv/4an3zyCdLT09GhQwcMGjQI+fn5tvuDkCxxrW8iarZ+//13iKKI+++/v879fvjhB/z666+4fPmyIblu2rQJnTp1ws8//4wHH3wQQGW1rI0bN8Ld3R1AZfGSPXv2YNGiRfD09IRKpYKLiwtatmxZ4xoLFizAwIEDAQDFxcVYtWoVNm7ciCFDhgAAPvroI6SkpGD9+vWYOXOm1f4GJH9sURNRsyX+bwXl+upjZ2RkICgoqFoLOCwsDF5eXsjIyDBsa9u2rSFJA+bVmY6IiDD8++LFi9BoNIiOjjZsc3R0RM+ePatdj5oHJmoiarY6duwIQRDqTX6iKBpN5ndvN1Znuraa1He7s+u9th8QtcVBTRsTNRE1W97e3hg0aBBWrFiB4uLiGq9XTWcKCwtDZmYmrl69anjt7NmzKCgoQGhoqMnXU6lU0Ol09e7XoUMHqFQqHDhwwLBNo9Hg+PHjZl2PmgYmaiJq1lauXAmdToeePXvi66+/xoULF5CRkYHly5cjMjISADBgwAB07doVo0aNQnp6Oo4dO2YoQXlnl3V92rZti6NHj+KPP/5Abm5ura1tV1dXvPTSS5g5cyaSk5Nx9uxZTJgwASUlJYiLizP5etnZ2Th58iR+//13AMCpU6dw8uRJDkizM0zURNSshYSEID09Hf369cOrr76Kzp07Y+DAgdizZw9WrVoFoLILeseOHWjRogX69OmDAQMGoF27dti6datZ15oxYwaUSiXCwsLg5+eHzMzMWvddvHgxnnrqKYwePRoPPPAAfv/9d+zatQstWrQw+XqrV69Gjx49MGHCBABAnz590KNHD3zzzTdmxU3SYj1qIiIiGWOLmoiISMaYqImIiGSMiZqIiEjGmKiJiIhkjImaiIhIxpioiYiIZIyJmoiISMaYqImIiGSMiZqIiEjGmKiJiIhkjImaiIhIxpioiYiIZOz/AaGGYW/07clpAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_baseline.mean_diff.plot(sankey_kwargs = {\"alpha\": 0.2});" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e225358c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/04-mini_meta_delta.ipynb b/nbs/tutorials/04-mini_meta_delta.ipynb new file mode 100644 index 00000000..8dca8cca --- /dev/null +++ b/nbs/tutorials/04-mini_meta_delta.ipynb @@ -0,0 +1,988 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2985aa19", + "metadata": {}, + "source": [ + "# Mini-Meta Delta\n", + "\n", + "> Explanation of how to compute the meta-analyzed weighted effect size using dabest.\n", + "\n", + "- order: 4" + ] + }, + { + "cell_type": "markdown", + "id": "a9ca4dd5", + "metadata": {}, + "source": [ + "When scientists perform replicates of the same experiment, the effect size of each replicate often varies, which complicates interpretation of the results. As of v2023.02.14, DABEST can now compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.\n", + "\n", + "This function uses the generic *inverse-variance* method to calculate the effect size, as follows:\n", + "\n", + "$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$\n", + "\n", + "where:\n", + "\n", + "\n", + "$\\hat{\\theta_{i}} = \\text{Mean difference for replicate }i$\n", + "\n", + "\n", + "$w_{i} = \\text{Weight for replicate }i = \\frac{1}{s_{i}^2}$ \n", + "\n", + "\n", + "$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$\n", + "\n", + "\n", + "$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$\n" + ] + }, + { + "cell_type": "markdown", + "id": "5fb1dc0f", + "metadata": {}, + "source": [ + "Note that this uses the *fixed-effects* model of meta-analysis, as opposed to the random-effects model; that is to say, all variation between the results of each replicate is assumed to be due solely to sampling error. We thus recommend that this function only be used for replications of the same experiment, i.e. situations where it can be safely assumed that each replicate estimates the same population mean $\\mu$. \n", + "\n", + "Also note that as of v2023.02.14, DABEST can only compute weighted effect size *for mean difference only*, and not standardized measures such as Cohen's *d*.\n", + "\n", + "For more information on meta-analysis, please refer to Chapter 10 of the Cochrane handbook: https://training.cochrane.org/handbook/current/chapter-10\n" + ] + }, + { + "cell_type": "markdown", + "id": "12c4d226", + "metadata": {}, + "source": [ + "## Load Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c7d7eaa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We're using DABEST v2023.02.14\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "print(\"We're using DABEST v{}\".format(dabest.__version__))" + ] + }, + { + "cell_type": "markdown", + "id": "4a4f0bde", + "metadata": {}, + "source": [ + "## Create dataset for mini-meta demo" + ] + }, + { + "cell_type": "markdown", + "id": "09a9b692", + "metadata": {}, + "source": [ + "We will now create a dataset to demonstrate the mini-meta function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b80a5aa", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm # Used in generation of populations.\n", + "\n", + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "# pop_size = 10000 # Size of each population.\n", + "Ns = 20 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns/2).tolist()\n", + "males = np.repeat('Male', Ns/2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns+1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3,\n", + " 'Gender' : gender, 'ID' : id_col\n", + " })" + ] + }, + { + "cell_type": "markdown", + "id": "e5b9dbbd", + "metadata": {}, + "source": [ + "We now have 3 Control and 3 Test groups, simulating 3 replicates of the same experiment. Our\n", + "dataset also has a non-numerical column indicating gender, and another\n", + "column indicating the identity of each observation." + ] + }, + { + "cell_type": "markdown", + "id": "a9e6d91f", + "metadata": {}, + "source": [ + "This is known as a 'wide' dataset. See this\n", + "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/)\n", + "for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef976b60", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Control 1Test 1Control 2Test 2Control 3Test 3GenderID
02.7939843.4208753.3246611.7074673.8169401.796581Female1
13.2367593.4679723.6851861.1218463.7503583.944566Female2
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" + ], + "text/plain": [ + " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Gender ID\n", + "0 2.793984 3.420875 3.324661 1.707467 3.816940 1.796581 Female 1\n", + "1 3.236759 3.467972 3.685186 1.121846 3.750358 3.944566 Female 2\n", + "2 3.019149 4.377179 5.616891 3.301381 2.945397 2.832188 Female 3\n", + "3 2.804638 4.564780 2.773152 2.534018 3.575179 3.048267 Female 4\n", + "4 2.858019 3.220058 2.550361 2.796365 3.692138 3.276575 Female 5" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "21171074", + "metadata": {}, + "source": [ + "## Loading Data" + ] + }, + { + "cell_type": "markdown", + "id": "adc6d626", + "metadata": {}, + "source": [ + "Next, we load data as we would normally using ``dabest.load()``. This time, however,\n", + "we also specify the argument ``mini_meta=True``. As we are loading three experiments' worth of data,\n", + "``idx`` is passed as a tuple of tuples, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6701c83", + "metadata": {}, + "outputs": [], + "source": [ + "unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1a3bcd5c", + "metadata": {}, + "source": [ + "When this ``Dabest`` object is called, it should show that effect sizes will be calculated for each group, as well as the weighted delta. Note once again that weighted delta will only be calcuated for mean difference.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cafcafd2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:59:33 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. Test 1 minus Control 1\n", + "2. Test 2 minus Control 2\n", + "3. Test 3 minus Control 3\n", + "4. weighted delta (only for mean difference)\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unpaired" + ] + }, + { + "cell_type": "markdown", + "id": "f52ddc8e", + "metadata": {}, + "source": [ + "By calling the ``mean_diff`` attribute, you can view the mean differences for each group as well as the weighted delta.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "535d1163", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 22:59:27 2023.\n", + "\n", + "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n", + "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.895].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between Control 3 and Test 3 is -0.255 [95%CI -0.717, 0.196].\n", + "The p-value of the two-sided permutation t-test is 0.293, calculated for legacy purposes only. \n", + "\n", + "The weighted-average unpaired mean differences is -0.0104 [95%CI -0.222, 0.215].\n", + "The p-value of the two-sided permutation t-test is 0.937, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unpaired.mean_diff" + ] + }, + { + "cell_type": "markdown", + "id": "0de6f65c", + "metadata": {}, + "source": [ + "You can view the details of each experiment by accessing `.mean_diff.results`, as follows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9cb2caa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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controltestcontrol_Ntest_Neffect_sizeis_paireddifferencecibca_lowbca_highbca_interval_idxpct_lowpct_highpct_interval_idxbootstrapsresamplesrandom_seedpermutationspvalue_permutationpermutation_countpermutations_varpvalue_welchstatistic_welchpvalue_students_tstatistic_students_tpvalue_mann_whitneystatistic_mann_whitney
0Control 1Test 12020mean differenceNone0.480290950.2208690.767721(140, 4889)0.2156970.761716(125, 4875)[0.6686169333655454, 0.4382051534234943, 0.665...500012345[-0.17259843762502491, 0.03802293852634886, -0...0.00105000[0.026356588154404337, 0.027102495439046997, 0...0.002094-3.3088060.002057-3.3088060.00162583.0
1Control 2Test 22020mean differenceNone-1.38108595-1.925232-0.894537(108, 4857)-1.903964-0.875420(125, 4875)[-1.1603841133810318, -1.6359724856206515, -1....500012345[0.015164519971271773, 0.017231919606192303, -...0.00005000[0.12241741427801064, 0.12241565174150129, 0.1...0.0000115.1388400.0000095.1388400.000026356.0
2Control 3Test 32020mean differenceNone-0.25483195-0.7173370.196121(115, 4864)-0.7103460.206131(125, 4875)[-0.09556572841011901, 0.35166073097757433, -0...500012345[-0.05901068591042824, -0.13617667681797307, 0...0.29345000[0.058358897501663703, 0.05796253365278035, 0....0.2947661.0697980.2914591.0697980.285305240.0
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" + ], + "text/plain": [ + " control test control_N test_N effect_size is_paired \\\n", + "0 Control 1 Test 1 20 20 mean difference None \n", + "1 Control 2 Test 2 20 20 mean difference None \n", + "2 Control 3 Test 3 20 20 mean difference None \n", + "\n", + " difference ci bca_low bca_high bca_interval_idx pct_low pct_high \\\n", + "0 0.480290 95 0.220869 0.767721 (140, 4889) 0.215697 0.761716 \n", + "1 -1.381085 95 -1.925232 -0.894537 (108, 4857) -1.903964 -0.875420 \n", + "2 -0.254831 95 -0.717337 0.196121 (115, 4864) -0.710346 0.206131 \n", + "\n", + " pct_interval_idx bootstraps \\\n", + "0 (125, 4875) [0.6686169333655454, 0.4382051534234943, 0.665... \n", + "1 (125, 4875) [-1.1603841133810318, -1.6359724856206515, -1.... \n", + "2 (125, 4875) [-0.09556572841011901, 0.35166073097757433, -0... \n", + "\n", + " resamples random_seed permutations \\\n", + "0 5000 12345 [-0.17259843762502491, 0.03802293852634886, -0... \n", + "1 5000 12345 [0.015164519971271773, 0.017231919606192303, -... \n", + "2 5000 12345 [-0.05901068591042824, -0.13617667681797307, 0... \n", + "\n", + " pvalue_permutation permutation_count \\\n", + "0 0.0010 5000 \n", + "1 0.0000 5000 \n", + "2 0.2934 5000 \n", + "\n", + " permutations_var pvalue_welch \\\n", + "0 [0.026356588154404337, 0.027102495439046997, 0... 0.002094 \n", + "1 [0.12241741427801064, 0.12241565174150129, 0.1... 0.000011 \n", + "2 [0.058358897501663703, 0.05796253365278035, 0.... 0.294766 \n", + "\n", + " statistic_welch pvalue_students_t statistic_students_t \\\n", + "0 -3.308806 0.002057 -3.308806 \n", + "1 5.138840 0.000009 5.138840 \n", + "2 1.069798 0.291459 1.069798 \n", + "\n", + " pvalue_mann_whitney statistic_mann_whitney \n", + "0 0.001625 83.0 \n", + "1 0.000026 356.0 \n", + "2 0.285305 240.0 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.options.display.max_columns = 50\n", + "unpaired.mean_diff.results" + ] + }, + { + "cell_type": "markdown", + "id": "c581f3fa", + "metadata": {}, + "source": [ + "Note, however, that this does not contain the relevant information for our weighted delta. The details of the weighted delta are stored as attributes of the ``mini_meta_delta`` object, for example:\n", + "\n", + " - ``group_var``: the pooled group variances of each set of 2 experiment groups\n", + " - ``difference``: the weighted mean difference calculated based on the raw data\n", + " - ``bootstraps``: the deltas of each set of 2 experiment groups calculated based on the bootstraps\n", + " - ``bootstraps_weighted_delta``: the weighted deltas calculated based on the bootstraps\n", + " - ``permutations``: the deltas of each set of 2 experiment groups calculated based on the permutation data\n", + " - ``permutations_var``: the pooled group variances of each set of 2 experiment groups calculated based on permutation data\n", + " - ``permutations_weighted_delta``: the weighted deltas calculated based on the permutation data\n", + "\n", + "You can call each of the above attributes on their own:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c867467", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.010352287701068538" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unpaired.mean_diff.mini_meta_delta.difference" + ] + }, + { + "cell_type": "markdown", + "id": "5eafcc8e", + "metadata": {}, + "source": [ + "Attributes of the weighted delta can also be written to a `dict` by using the ``.to_dict()`` function. Below, we do this and subsequently convert the dict into a dataframe for better readability:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d30d3b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0
acceleration_value0.000193
alpha0.05
bca_high0.215037
bca_interval_idx(128, 4878)
bca_low-0.221666
bias_correction0.005013
bootstraps[[0.6686169333655454, 0.4382051534234943, 0.66...
bootstraps_weighted_delta[0.1771640316740503, 0.055052653330973, 0.1635...
ci95
control[Control 1, Control 2, Control 3]
control_N[20, 20, 20]
control_var[0.17628013404546256, 0.9584767911266554, 0.16...
difference-0.010352
group_var[0.021070042637349427, 0.07222883451891535, 0....
jackknives[-0.008668330406027464, -0.008643903244926629,...
pct_high0.213769
pct_interval_idx(125, 4875)
pct_low-0.222307
permutation_count5000
permutations[[-0.17259843762502491, 0.03802293852634886, -...
permutations_var[[0.026356588154404337, 0.027102495439046997, ...
permutations_weighted_delta[-0.11757207833491819, -0.012928679700934625, ...
pvalue_permutation0.9374
test[Test 1, Test 2, Test 3]
test_N[20, 20, 20]
test_var[0.245120718701526, 0.4860998992516514, 0.9667...
\n", + "
" + ], + "text/plain": [ + " 0\n", + "acceleration_value 0.000193\n", + "alpha 0.05\n", + "bca_high 0.215037\n", + "bca_interval_idx (128, 4878)\n", + "bca_low -0.221666\n", + "bias_correction 0.005013\n", + "bootstraps [[0.6686169333655454, 0.4382051534234943, 0.66...\n", + "bootstraps_weighted_delta [0.1771640316740503, 0.055052653330973, 0.1635...\n", + "ci 95\n", + "control [Control 1, Control 2, Control 3]\n", + "control_N [20, 20, 20]\n", + "control_var [0.17628013404546256, 0.9584767911266554, 0.16...\n", + "difference -0.010352\n", + "group_var [0.021070042637349427, 0.07222883451891535, 0....\n", + "jackknives [-0.008668330406027464, -0.008643903244926629,...\n", + "pct_high 0.213769\n", + "pct_interval_idx (125, 4875)\n", + "pct_low -0.222307\n", + "permutation_count 5000\n", + "permutations [[-0.17259843762502491, 0.03802293852634886, -...\n", + "permutations_var [[0.026356588154404337, 0.027102495439046997, ...\n", + "permutations_weighted_delta [-0.11757207833491819, -0.012928679700934625, ...\n", + "pvalue_permutation 0.9374\n", + "test [Test 1, Test 2, Test 3]\n", + "test_N [20, 20, 20]\n", + "test_var [0.245120718701526, 0.4860998992516514, 0.9667..." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weighted_delta_details = unpaired.mean_diff.mini_meta_delta.to_dict()\n", + "weighted_delta_df = pd.DataFrame.from_dict(weighted_delta_details, orient = 'index')\n", + "weighted_delta_df" + ] + }, + { + "cell_type": "markdown", + "id": "7797244d", + "metadata": {}, + "source": [ + "## Producing estimation plots - unpaired data" + ] + }, + { + "cell_type": "markdown", + "id": "d51a505d", + "metadata": {}, + "source": [ + "Simply passing the ``.plot()`` method will produce a **Cumming estimation plot** showing the data for each experimental replicate as well as the calculated weighted delta.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ffaa157", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unpaired.mean_diff.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "a382d182", + "metadata": {}, + "source": [ + "You can also hide the weighted delta by passing the argument ``show_mini_meta=False``. In this case, the resulting graph would be identical to a multiple two-groups plot:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91488409", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unpaired.mean_diff.plot(show_mini_meta=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e132dfd1", + "metadata": {}, + "source": [ + "## Producing estimation plots - paired data" + ] + }, + { + "cell_type": "markdown", + "id": "9103409b", + "metadata": {}, + "source": [ + "The tutorial up to this point has dealt with unpaired data. If your data is paired data, the process for loading, plotting and accessing the data is the same as for unpaired data, except the argument ``paired = \"sequential\" or \"baseline\"`` and an appropriate ``id_col`` are passed during the ``dabest.load()`` step, as follows:\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b0feff8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n", + "paired.mean_diff.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c5081cc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/05-deltadelta.ipynb b/nbs/tutorials/05-deltadelta.ipynb new file mode 100644 index 00000000..6bd5d03f --- /dev/null +++ b/nbs/tutorials/05-deltadelta.ipynb @@ -0,0 +1,697 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cf1612f8", + "metadata": {}, + "source": [ + "# Delta - Delta\n", + "\n", + "> Explanation of how to calculate delta-delta using dabest.\n", + "\n", + "- order: 5" + ] + }, + { + "cell_type": "markdown", + "id": "cfdb7e31", + "metadata": {}, + "source": [ + "Since version 2023.02.14, DABEST also supports the calculation of delta-delta, an experimental function that allows the comparison between two bootstrapped effect sizes computed from two independent categorical variables. \n", + "\n", + "Many experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size lets us distill the net effect of the two variables. To illustrate this, let's delve into the following problem. \n", + "\n", + "Consider an experiment where we test the efficacy of a drug named ``Drug`` on a disease-causing mutation ``M`` based on disease metric ``Y``. The greater value ``Y`` has the more severe the disease phenotype is. Phenotype ``Y`` has been shown to be caused by a gain of function mutation ``M``, so we expect a difference between wild type (``W``) subjects and mutant subjects (``M``). Now, we want to know whether this effect is ameliorated by the administration of ``Drug`` treatment. We also administer a placebo as a control. In theory, we only expect ``Drug`` to have an effect on the ``M`` group, although in practice many drugs have non-specific effects on healthy populations too.\n", + "\n", + "Effectively, we have 4 groups of subjects for comparison. \n" + ] + }, + { + "cell_type": "markdown", + "id": "7a202204", + "metadata": {}, + "source": [ + "| | Wildtype | Mutant |\n", + "|-------|---------|----------|\n", + "| Drug | XD, W | XD, M |\n", + "| Placebo | XP, W | XP, M |" + ] + }, + { + "cell_type": "markdown", + "id": "be4d9084", + "metadata": {}, + "source": [ + "There are 2 ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are 2 ``Genotype``\\s: ``W`` (wild type population) and ``M`` (mutant population). In addition, each experiment was done twice (``Rep1`` and ``Rep2``). We shall do a few analyses to visualise these differences in a simulated dataset. \n" + ] + }, + { + "cell_type": "markdown", + "id": "9ec30d58", + "metadata": {}, + "source": [ + "## Load Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fdd66d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We're using DABEST v2023.02.14\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "print(\"We're using DABEST v{}\".format(dabest.__version__))" + ] + }, + { + "cell_type": "markdown", + "id": "96a35aa6", + "metadata": {}, + "source": [ + "## Simulate a dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "729207f7", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm # Used in generation of populations.\n", + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "\n", + "# Create samples\n", + "N = 20\n", + "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", + "y[N:2*N] = y[N:2*N]+1\n", + "y[2*N:3*N] = y[2*N:3*N]-0.5\n", + "\n", + "# Add a `Treatment` column\n", + "t1 = np.repeat('Placebo', N*2).tolist()\n", + "t2 = np.repeat('Drug', N*2).tolist()\n", + "treatment = t1 + t2 \n", + "\n", + "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", + "rep = []\n", + "for i in range(N*2):\n", + " rep.append('Rep1')\n", + " rep.append('Rep2')\n", + "\n", + "# Add a `Genotype` column as the second variable\n", + "wt = np.repeat('W', N).tolist()\n", + "mt = np.repeat('M', N).tolist()\n", + "wt2 = np.repeat('W', N).tolist()\n", + "mt2 = np.repeat('M', N).tolist()\n", + "\n", + "\n", + "genotype = wt + mt + wt2 + mt2\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id = list(range(0, N*2))\n", + "id_col = id + id \n", + "\n", + "\n", + "# Combine all columns into a DataFrame.\n", + "df_delta2 = pd.DataFrame({'ID' : id_col,\n", + " 'Rep' : rep,\n", + " 'Genotype' : genotype, \n", + " 'Treatment': treatment,\n", + " 'Y' : y\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c00f10e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDRepGenotypeTreatmentY
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" + ], + "text/plain": [ + " ID Rep Genotype Treatment Y\n", + "0 0 Rep1 W Placebo 2.793984\n", + "1 1 Rep2 W Placebo 3.236759\n", + "2 2 Rep1 W Placebo 3.019149\n", + "3 3 Rep2 W Placebo 2.804638\n", + "4 4 Rep1 W Placebo 2.858019" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_delta2.head()" + ] + }, + { + "cell_type": "markdown", + "id": "50d94de3", + "metadata": {}, + "source": [ + "## Unpaired Data" + ] + }, + { + "cell_type": "markdown", + "id": "f4315e6f", + "metadata": {}, + "source": [ + "To make a delta-delta plot, you need to simply set ``delta2 = True`` in the \n", + "``dabest.load()`` function. However, here ``x`` needs to be declared as a list\n", + "consisting of 2 elements rather than 1 in most of the cases. The first element\n", + "in ``x`` will be the variable plotted along the horizontal axis, and the second\n", + "one will determine the colour of dots for scattered plots or the colour of lines\n", + "for slopegraphs. We use the ``experiment`` input to specify grouping of the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36a5e3fd", + "metadata": {}, + "outputs": [], + "source": [ + "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")" + ] + }, + { + "cell_type": "markdown", + "id": "3018f94e", + "metadata": {}, + "source": [ + "The above function creates the following object: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5499575", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 23:07:31 2023.\n", + "\n", + "Effect size(s) with 95% confidence intervals will be computed for:\n", + "1. M Placebo minus W Placebo\n", + "2. M Drug minus W Drug\n", + "3. Drug minus Placebo (only for mean difference)\n", + "\n", + "5000 resamples will be used to generate the effect size bootstraps." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unpaired_delta2" + ] + }, + { + "cell_type": "markdown", + "id": "f720abcf", + "metadata": {}, + "source": [ + "\n", + "We can quickly check out the effect sizes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e9cc16d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2023.02.14\n", + "==================\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 19 23:07:42 2023.\n", + "\n", + "The unpaired mean difference between W Placebo and M Placebo is 1.23 [95%CI 0.948, 1.52].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "The unpaired mean difference between W Drug and M Drug is 0.326 [95%CI 0.0934, 0.584].\n", + "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n", + "\n", + "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.26, -0.535].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing the effect size (or greater),\n", + "assuming the null hypothesis of zero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", + "\n", + "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unpaired_delta2.mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7dbda11b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unpaired_delta2.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "1a3e7ca1", + "metadata": {}, + "source": [ + "In the above plot, the horizontal axis represents the ``Genotype`` condition\n", + "and the dot colour is also specified by ``Genotype``. The left pair of \n", + "scattered plots is based on the ``Placebo`` group while the right pair is based\n", + "on the ``Drug`` group. The bottom left axis contains the two primary deltas: the ``Placebo`` delta \n", + "and the ``Drug`` delta. We can easily see that when only the placebo was \n", + "administered, the mutant phenotype is around 1.23 [95%CI 0.948, 1.52]. This difference was shrunken to around 0.326 [95%CI 0.0934, 0.584] when the drug was administered. This gives us some indication that the drug is effective in amiliorating the disease phenotype. Since the ``Drug`` did not completely eliminate the mutant phenotype, we have to calculate how much net effect the drug had. This is where ``delta-delta`` comes in. We use the ``Placebo`` delta as a reference for how much the mutant phenotype is supposed to be, and we subtract the ``Drug`` delta from it. The bootstrapped mean differences (delta-delta) between the ``Placebo`` \n", + "and ``Drug`` group are plotted at the right bottom with a separate y-axis from other bootstrap plots. \n", + "This effect size, at about -0.903 [95%CI -1.26, -0.535], is the net effect size of the drug treatment. That is to say that treatment with drug A reduced disease phenotype by 0.903.\n", + "\n", + "Mean difference between mutants and wild types given the placebo treatment is:\n", + "\n", + "$\\Delta_{1} = \\overline{X}_{P, M} - \\overline{X}_{P, W}$\n", + "\n", + "Mean difference between mutants and wild types given the drug treatment is:\n", + "\n", + "\n", + "$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{D, W}$\n", + "\n", + "The net effect of the drug on mutants is:\n", + " \n", + "\n", + "\n", + "$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$\n", + " \n", + "\n", + "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference." + ] + }, + { + "cell_type": "markdown", + "id": "054d04d2", + "metadata": {}, + "source": [ + "## Specifying Grouping for Comparisons" + ] + }, + { + "cell_type": "markdown", + "id": "58c98331", + "metadata": {}, + "source": [ + "In the example above, we used the convention of \"test - control' but you can manipulate the orders of experiment groups as well as the horizontal axis variable by setting ``experiment_label`` and ``x1_level``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9398a01", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unpaired_delta2_specified = dabest.load(data = df_delta2, \n", + " x = [\"Genotype\", \"Genotype\"], y = \"Y\", \n", + " delta2 = True, experiment = \"Treatment\",\n", + " experiment_label = [\"Drug\", \"Placebo\"],\n", + " x1_level = [\"M\", \"W\"])\n", + "\n", + "unpaired_delta2_specified.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "d513187c", + "metadata": {}, + "source": [ + "## Paired Data" + ] + }, + { + "cell_type": "markdown", + "id": "fdc663cb", + "metadata": {}, + "source": [ + "The delta - delta function also supports paired data, which is useful for us to visualise the data in an alternate way. Assuming that the placebo and drug treatment were done on the same subjects, our data is paired between the treatment conditions. We can specify this by using ``Treatment`` as ``x`` and ``Genotype`` as ``experiment``, and we further specify that ``id_col`` is ``ID``, linking data from the same subject with each other. Since we have done two replicates of the experiments, we can also colour the slope lines according to ``Rep``. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0949bfea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "paired_delta2 = dabest.load(data = df_delta2, \n", + " paired = \"baseline\", id_col=\"ID\",\n", + " x = [\"Treatment\", \"Rep\"], y = \"Y\", \n", + " delta2 = True, experiment = \"Genotype\")\n", + "paired_delta2.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "5c7868a7", + "metadata": {}, + "source": [ + "We see that the drug had a non-specific effect of -0.321 [95%CI -0.498, -0.131] on wild type subjects even when they were not sick, and it had a bigger effect of -1.22 [95%CI -1.52, -0.906] in mutant subjects. In this visualisation, we can see the delta-delta value of -0.903 [95%CI -1.21, -0.587] as the net effect of the drug accounting for non-specific actions in healthy individuals. \n" + ] + }, + { + "cell_type": "markdown", + "id": "3b07192c", + "metadata": {}, + "source": [ + "Mean difference between drug and placebo treatments in wild type subjects is:\n", + "\n", + "$$\\Delta_{1} = \\overline{X}_{D, W} - \\overline{X}_{P, W}$$\n", + "\n", + "Mean difference between drug and placebo treatments in mutant subjects is:\n", + "\n", + "$$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{P, M}$$\n", + "\n", + "The net effect of the drug on mutants is:\n", + "\n", + "$$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", + "\n", + "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference." + ] + }, + { + "cell_type": "markdown", + "id": "e33f0064", + "metadata": {}, + "source": [ + "## Connection to ANOVA" + ] + }, + { + "cell_type": "markdown", + "id": "647eaa00", + "metadata": {}, + "source": [ + "The configuration of comparison we performed above is reminiscent of a two-way ANOVA. In fact, the delta - delta is an effect size estimated for the interaction term between ``Treatment`` and ``Genotype``. Main effects of ``Treatment`` and ``Genotype``, on the other hand, can be determined by simpler, univariate contrast plots. " + ] + }, + { + "cell_type": "markdown", + "id": "044a5fab", + "metadata": {}, + "source": [ + "## Omitting Delta-delta Plot" + ] + }, + { + "cell_type": "markdown", + "id": "226337e9", + "metadata": {}, + "source": [ + "If for some reason you don't want to display the delta-delta plot, you can easily do so by \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3230fae7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unpaired_delta2.mean_diff.plot(show_delta2=False);" + ] + }, + { + "cell_type": "markdown", + "id": "0b3a3da4", + "metadata": {}, + "source": [ + "## Other Effect Sizes" + ] + }, + { + "cell_type": "markdown", + "id": "5cb9650b", + "metadata": {}, + "source": [ + "\n", + "Since the delta-delta function is only applicable to mean differences, plots \n", + "of other effect sizes will not include a delta-delta bootstrap plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7b6b505", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unpaired_delta2.cohens_d.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "b71ec4b4", + "metadata": {}, + "source": [ + "## Statistics" + ] + }, + { + "cell_type": "markdown", + "id": "4ed26036", + "metadata": {}, + "source": [ + "You can find all outputs of the delta - delta calculation by assessing the attribute named ``delta_delta`` of the \n", + "effect size object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "205b0b55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v0.0.1\n", + "=============\n", + " \n", + "Good evening!\n", + "The current time is Sun Mar 12 00:55:42 2023.\n", + "\n", + "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.26, -0.535].\n", + "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", + "\n", + "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", + "Any p-value reported is the probability of observing theeffect size (or greater),\n", + "assuming the null hypothesis ofzero difference is true.\n", + "For each p-value, 5000 reshuffles of the control and test labels were performed." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unpaired_delta2.mean_diff.delta_delta" + ] + }, + { + "cell_type": "markdown", + "id": "3ba800cc", + "metadata": {}, + "source": [ + "``delta_delta`` has its own attributes, containing various information of delta - delta.\n", + "\n", + " - ``difference``: the mean bootstrapped differences between the 2 groups of bootstrapped mean differences \n", + " - ``bootstraps``: the 2 groups of bootstrapped mean differences \n", + " - ``bootstraps_delta_delta``: the bootstrapped differences between the 2 groups of bootstrapped mean differences \n", + " - ``permutations``: the mean difference between the two groups of bootstrapped mean differences calculated based on the permutation data\n", + " - ``permutations_var``: the pooled group variances of two groups of bootstrapped mean differences calculated based on permutation data\n", + " - ``permutations_delta_delta``: the delta-delta calculated based on the permutation data\n", + "\n", + "``delta_delta.to_dict()`` will return to you all the attributes in a dictionary format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddefa6e4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/06-plotaesthetics.ipynb b/nbs/tutorials/06-plotaesthetics.ipynb new file mode 100644 index 00000000..41375b80 --- /dev/null +++ b/nbs/tutorials/06-plotaesthetics.ipynb @@ -0,0 +1,792 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f833a32", + "metadata": {}, + "source": [ + "# Controlling Plot Aesthetics\n", + "\n", + "- order: 5" + ] + }, + { + "cell_type": "markdown", + "id": "7879a287", + "metadata": {}, + "source": [ + "Changing the y-axes labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d374d47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We're using DABEST v2023.02.14\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import dabest\n", + "\n", + "print(\"We're using DABEST v{}\".format(dabest.__version__))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab12ec7f", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm # Used in generation of populations.\n", + "\n", + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "# pop_size = 10000 # Size of each population.\n", + "Ns = 20 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns/2).tolist()\n", + "males = np.repeat('Male', Ns/2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns+1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3,\n", + " 'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,\n", + " 'Gender' : gender, 'ID' : id_col\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e3b1021", + "metadata": {}, + "outputs": [], + "source": [ + " two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000)" + ] + }, + { + "cell_type": "markdown", + "id": "eea91eac", + "metadata": {}, + "source": [ + "Changing the y-axes labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54a3445d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_unpaired.mean_diff.plot(swarm_label=\"This is my\\nrawdata\",\n", + " contrast_label=\"The bootstrap\\ndistribtions!\");" + ] + }, + { + "cell_type": "markdown", + "id": "8d0f7aed", + "metadata": {}, + "source": [ + "Color the rawdata according to another column in the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "527b475b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n", + " (\"Control 2\", \"Test 2\")\n", + " ))\n", + "multi_2group.mean_diff.plot(color_col=\"Gender\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "562245e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", + " paired=\"baseline\", id_col=\"ID\")\n", + "two_groups_paired_baseline.mean_diff.plot(color_col=\"Gender\");" + ] + }, + { + "cell_type": "markdown", + "id": "bccd01be", + "metadata": {}, + "source": [ + "Changing the palette used with `custom_palette`. Any valid matplotlib or seaborn color palette is accepted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a6a82fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group.mean_diff.plot(custom_palette=\"Paired\");" + ] + }, + { + "cell_type": "markdown", + "id": "5d1c2921", + "metadata": {}, + "source": [ + "You can also create your own color palette. Create a dictionary where\n", + "the keys are group names, and the values are valid matplotlib colors.\n", + "\n", + "You can specify matplotlib colors in a [variety of\n", + "ways](https://matplotlib.org/users/colors.html). Here, I demonstrate\n", + "using named colors, hex strings (commonly used on the web), and RGB\n", + "tuples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33271a43", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_color_palette = {\"Control 1\" : \"blue\",\n", + " \"Test 1\" : \"purple\",\n", + " \"Control 2\" : \"#cb4b16\", # This is a hex string.\n", + " \"Test 2\" : (0., 0.7, 0.2) # This is a RGB tuple.\n", + " }\n", + "\n", + "multi_2group.mean_diff.plot(custom_palette=my_color_palette);" + ] + }, + { + "cell_type": "markdown", + "id": "032b975b", + "metadata": {}, + "source": [ + "By default, ``dabest.plot()`` will\n", + "[desaturate](https://en.wikipedia.org/wiki/Colorfulness#Saturation)\n", + "the color of the dots in the swarmplot by 50%. This draws attention to\n", + "the effect size bootstrap curves.\n", + "\n", + "You can alter the default values with the ``swarm_desat`` and\n", + "``halfviolin_desat`` keywords.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3db70141", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group.mean_diff.plot(custom_palette=my_color_palette,\n", + " swarm_desat=0.75,\n", + " halfviolin_desat=0.25);" + ] + }, + { + "cell_type": "markdown", + "id": "9547d1aa", + "metadata": {}, + "source": [ + "You can also change the sizes of the dots used in the rawdata swarmplot,\n", + "and those used to indicate the effect sizes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e964805", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group.mean_diff.plot(raw_marker_size=3,\n", + " es_marker_size=12);" + ] + }, + { + "cell_type": "markdown", + "id": "21949c5f", + "metadata": {}, + "source": [ + "Changing the y-limits for the rawdata axes, and for the contrast axes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97d2052e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group.mean_diff.plot(swarm_ylim=(0, 5),\n", + " contrast_ylim=(-2, 2));" + ] + }, + { + "cell_type": "markdown", + "id": "4688b5c9", + "metadata": {}, + "source": [ + "If your effect size is qualitatively inverted (ie. a smaller value is a\n", + "better outcome), you can simply invert the tuple passed to\n", + "``contrast_ylim``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63e2465a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FC7EYA3v888Po3LUcFJdXqd3/9OVM1NXLILEy6TtfRCYjMjgS6+avw56UPcguzIa7gztyinOw+fhm5T7nrp/DzqSdWDB+AUb1GoX4c/FYs2uNyusoBAX2puyF1EqK+ePm33U8n8/5/K6PJdKUzm+F7Ny5E++//z78/PxUtnfu3BkZGRm6Pj39jXeeiPTLwcYBU/pNwbyx89DFtwt2ndnVaB+FoMC/t/0bxRXF+O3Yb2pfa/eZ3SitLFXbTmRMdP4VtrKyEra2jVdiLCgoUCleRXevi78XXBxs1fZaRHYOgFTC3goiQ9l5ZqfaNplCht1nduNK7hW1+9TL65GWl4benXrrIjy6S/WVlUiPj0dpRgakTk4IGjoUDr6+hg7L4HTeYzFkyBCsX79e+VwkEkGhUOCDDz7AsGHDdH36dsHK0gKzRvdvss3SQqy2jYj0o7iFqbfFlcWQWjX/RcvO2u6uz7/gmwWY9dksLPhmwV2/BqkquHABf82bh6Rvv8W1PXtwYdMmxD3/PC5s3tzywWZO519jP/jgAwwdOhQJCQmoq6vDq6++inPnzqGoqAiHDx/W9enbjYmDesLK0hI/7TqhnBnSxd8LT04cjB7BnN5GZEj+7v5IzkhW2x7oEYih3YdiR9KOJtt9XX3RxffuFxosrihGYXnhXR9PquR1dTjywQeov3PFbEFAyo8/wq1LF3h0726Y4IyAzhOLsLAwJCcnY9WqVbCwsEBlZSWmTZuGefPmwcfHR9enb1fG9O+O0f3CkFtYCksLi0bTUonIMMZHjcf2xO1QCIpGbY62jojpEYOoTlFIvJqIm2U3VdqtLKzw7OhnIRKJ9BUutSDr2DHUlqof85K2YwcTC13z9vY2y+XLjZFIJIKvu7OhwzArtaU3IUCAtVPzlU/N0Y3kPcg79Reqi3IgcXCHd+Ro+ESNg0hs/mvztOTc9XMNgyqrShHoEYixvcfCs4n/I/ml+ZBaSvHixBfx+V+fo15er2xzsnVC7IxYWFtZw9rKGp888Qk2H9+MQ+cPoU5Wh4igCNwffb9ySmplTSW2n96OoxePQq6Qo3dwb0yImgBXB1e9vW8CKvLymm0vb6Hd3Ok8sThw4ECz7UOGDNF1CER3pejyCWTs/x6VN64CAGw9AhEQMxPuXQep7Cevq0Z1YTYsbRxg7exliFDvSkXuZWQd3YTSjGSILSVw7zYIHQZMg8S+4ZdUWtwq5CZsVe4vqy7H1R1foTQjGV3vWwSRqP2uCLBm1xqVqaPHLh3D5uOb8cb9b6BvSF8AwOmrp7F231rloEwfFx88OeJJyAW5so7FkLAhKmMrXO1dMefeOZhz75xG5yyuKMar619FdtE/Be8u5VxC3Ok4vDfrPQS4B+jq7dIdbN3cmm93d9dTJMZJ54nF0KFDG227vUtPLm+8AiCRoRVdOYnzv7wN3NZ1XXUzAxd+XYGu970G926DoZDLkLFvHfIS4yD/uxCSo18Ygsc8Dfvbih7drbqKImQd3YSC8wehkNXBKbAH/KLvg0OHti9gVZx2Cud/WQbhtsWyso9tRsH5Q4h47EPUV5WqJBW3K7xwBMVXEuDauV+b4zBFJ6+cVEkqbqmT1WHl5pVY//x6XMm9gtgNsZAp/vn3zS3Oxaodq7Bg/AI8ce8TGp937d61KknFLSWVJfjPtv/g/Uff1/g16e74DxyIM999h/qqpmfiBY8YoeeIjIvOv3IUFxerPPLz8xEXF4e+ffti5071U7CIDClj//cqScU/BGTs/x6CIODK1s+RfWyzMqkAgLKs80j5YRFqiv/pChUEBcqun0fRlYRWL8xVW16IM2tfQs7xzagrL4CsugyFF44g+bt/ofDS8Va/j4rcy7h57gDKrp+/LR4BaXGrVJIK5XnLbiLz4E+4eS6+2de9ebb5dnMWdzpObVtlbSUOnj+IH+J/UEkqbvfjgR+bXFIdaOjleP2H1zFpxSQ88OED+GLbF7hZehN1sjocOK++9zclMwV5xe27+12fLG1sMODFF2EhkTRq6zJxInx6t+9pwTrvsXBycmq0beTIkZBKpXjxxRdx6tQpXYdApJHa8kJU5qWpba8uzELJtdPIT9nbZLu8phLZJ/5Ap9H/h+K0BKRtX4WakoYf+iKxJTwjhqPTmGcgtmz8Q+mW6wc3oLY0v9F2QSHD1bhVcO3ct9lbETXFubiweSUqci4pt9m6B6DL1FegqK9DTXGu2mNvno2HZ/hwte0AIKtTX+nV3N05uPJOucW5SMlMUdteWF6IK7lXENohVGX7vrP78NEfHykHeFbIK7A9cTtOXD6BJTOWoE5W1+x5S6pK4O3i3cp3QW3lHRmJMZ9/jqu7dv1Tx2LYMLh3bXuPoqkzWNUkDw8PXLx40VCnJ1KvFWVKyzLPAVC/X0naKVTkXsb5X95W6RkQFDLcSNoJhawOoVNeUXv8zXP71bbVlt1EWeZ5OAX2aLJdIatHyo9voPaOBayqCjJx7sc3ETzmWbWvDQCK+ho4dOiCvMRtavdx1MLtGFPl6+LbbDErHxfNZ7vVy+vx9a6vm5w1UlheiG2ntsHNwU3tlFGJpYSrphqArbs7ejz0kKHDMDo6vxWSnJys8jhz5gzi4uLwzDPPoGfPnro+PZHGpI7usPPqqLbd2tUXVvbND96CWIyso5uavN0AADfPHVC5XXI7QRAgr6tp9uXlzfQYFKQebJRU3FJfVYqqm+kQWaj/TmHv0xke3WMgVTMLxtLGAV6Ro5uNz5yNjxqvts3ZzhlDewxFeEC42n3cHNwQ4hOisi0lIwXFlepvkx1MPYiJfSaqbb83/F442HB6ORkHnScWvXr1QmRkJHr16qX8+7hx41BXV4dvvvlG16cnuisBMbMANbcaAmMegVuXfmrbAcCtywCUNlMQCYICpWq6y0UiERzu6CZXaRdbwt4nBIJCjoILh3Ftz7e4fmijMlG5fTxFUypvXINXhPrBZX7R90FsKUGPme/AzitYpc3axRfdH3obEjvnZs9hzsIDw/H48MchgmpdiVurmkosJXgk5hFYiptO3qb1n4YtJ7fg691fIy4xDtV11aitr232nDX1Nbh/4P0Y3atxQtc3pC/mjpp792+ISMt0fivk2rVrKs/FYjE8PDxgbW2t61Obnbp6GQ6cuYycghJ4uTpiSM8usJHe/VLKpJ5bl/7odv/ryNj/PapuNiyWZ+Pmh4AhM+HRvWGKtG/ficg58UejYyUObvDtNwk3zzU/1Vpsqb6Es9/A+5H6y9tNtnlG3AuFrB6J/30W1YVZyu0Z8T/Af/CDsJA0f21ZSKwRPPppKGT1yD+7TzlI1UJig4CYR+Aedk/D+3X1ReTcL1CWlYrqohxIHd3hFBjRYqEmyd9Lc0vMeInu6QOnY2DXgco6Fh09O2J4+HBl2e3wwHAsfXAp1u1bh8u5lwE0VM/sFdQL6/atU6ll8e3eb/HixBdhKbZUO+CzR0APiEViPD/hedwXfZ9KHYu2VOSktqktL0f63r0ozcxUrhXiFMBpvyJBaD/rXiYmJiIqKgqnTp1CbxMbtXv2ajaWrt2Kkop/ZiDY20jxxqPj0KdroAEjM3/VRbkABFi7+Kj8UhUEAdnHNyP3xBbUlt2ESGwJt9ABCBr+OKxdvHFtz7fIPtr0ipUWEhv0fPwT3Dwfj4qcS7C0todHj6FwCemrPEfuqW1I37sO8trKhoNEYnj0GIrO4+cj+btXUKHmPn/g8MeRsXet2vcTNiNWOVW0puQGSjNSILaSwqVTFCyljRcMpLa5WXoTMoUM9bJ6zFszr8kZIfbW9ogOjW5yBVSxSIy3H3obkcGRdx3DrM9mobC8EG4Obvj++e/v+nXoHzfPn8eh996D7I4pp91nzEDY9OkGiso46KTH4vPPP2/1vgsWcFGclpRV1uDNNX+gskZ1VHhFdS2Wrv0Ta19/DO5O9ir7W1iIYGfN1WO1wca16cF4IpEIfgOmoUP/KaivLIGFxAYWEhtle4f+U1Fw/hBqSxuPd/DsORJn1r6oMlX15rl4uIcNQejUVyASieETNQ6e4cNRfPUUFPW1cPTvAWtnT5RlpapNKgCgJC0BXr1G40YT6064dukPl5A+yufWzl4mVdTLVJRXl6O4shgejh7wcPIAAPx353/VTjOtqKlAJ+9OsLKwws6kncqeCxd7Fzw54sk2JRWkffLaWhz58MNGSQUAnNu4Ee5du8IzXP04G3Onk8Tik08+adV+IpGIiUUr7Dp5vlFScUtNnQzbj57FrDEDcCj5Cn7ceRxXsm9CJGpYLv2J8QMRGmB8U9Ce/egnFJdXwcXBFl++9LChw2kTkUisrFZ5O4m9CyIe+wDXD27AzbP7IK+rgb1vZ3QYMA3Xdq1RSSpuKTh/AM4de8H778GRClkd5DVVUMhqlQM2q25mNhtPVcF19HhkBRx8OyP31DblbQzvyDHw7TepXVfM1LWi8iJ8tfMr5a0KqZUU94bfiydHPInrBdebPTanKAfPjXsOM4fMxIXsC5BaSRERGAHLZgbakmFkHTuGurIyte1pO3YwsdC2O8dVUNtczS1osX13Qire//Gfb6iCACReysT59Bx89Nx0dPE3rm+lxeVVKCitaHlHEyd1cEPIuHkIGTcPgqCASCRG0eUTqGtmpckbSTvhHTkaWUf+h8wDP0FxW/0C1y794Rl+b7PnlNi7QCQSwbv3WHj3Hqu199JaSd88j7qKYkjsXdBrzmd6P7+hVNdV47UfXkPWbeNeautrsS1xG7IKs5pcR+R2rn8npy72LogOjdZprNQ2La0VUpHfuAZNe8KvLibAxb75+95Odjb49q+ml6CvqZNhfdwxXYRFGrrVU1Bb1nyiWFtWgJvnDiB97zqVpAIAii4dR0HqIUgc1K9F4NVrVNuDbYO6imLUlRe2usqoudibslclqbhdckYyOjYzhdlCbIF7I5pPGMl42Hk1/0XNzsNDT5EYJ730sWVlZWHLli3IzMxEXZ3qD8qPP/5YHyGYtJH9wrBxb4La9q6BXvjrqPpKfydT01Enk0FiyS5VY2DTQiEjG7cOyG5iLYpbClIPIXTqq7j856dQ1KvWu3AJ6Qvv3uO0Eidp5uSVk8223yy7ifuj78evR39V2S4WiTFv7Dy4ObRQG4WMhl90NJLWrUN9RdO9rsEjR+o5IuOi8980e/bswaRJk9CxY0dcvHgRPXr0QHp6OgRBMLmZGYYS6OWKx8YNxLptRxq1PTA8Cn4ezU/rUwgCFIp2M/nH6DkFRcDG3R/Vau65+0SNx4XfVqh/AUEBCyspev/fl8g99RfKsy/C0toOHt2Hwr3bIC5pbiB31rVoqv2Je59Aj4AeiDsdh4qaCvi7+2Nc73HKZdHJNFhKpYheuBCHV66EvEY1ue86dSq8e/UyTGBGQueJxaJFi/DSSy9h2bJlcHBwwG+//QZPT0/MnDkTY8aM0fXpzcbMkf3QPcgHW48kI6egFF6ujhgX3QN9uwahrl4GRztrlFU2Xa0xPLgDrCWsd2EsRCIRut3/Bs799BZq71h3wm/gdLh3GwRLG3vIqsvVvoaltT2snb3QsYVVMhWyOhRdOQlZdQUcOoTCzjNIG2+BmtA3pC+OX1a/QFw3v2744PcPcDD1IGRyGeyt7RHqG8rlzk2UV0QExn7xBa7t2fPPWiFDh8I1JKTlg82czhOL1NRU/Pzzzw0ns7REdXU17O3tsWzZMkyePBnPPPOMrkMwGVn5xYg7fg5FZZUI8HLF6P7d4eLwz/iKXp390auzf6PjJFaWePDevli95WCjNrFYhJmj2ufy1sbM1t0fUc+uxs1zB1B0+TgAEbx6jYLr31NBPXoMQ+7JLU0ea+3iA/sOocg5uQW5p7ahpjgX1k5e8Oo9Bh36TVb2WNw8F4+0uFUqCYpzcG+ETn0VViz/rHXDI4Zjy8ktyCxoPGunZ1BPrN+/XmUMRkVNBX479huyCrMQOyNWn6GSlti4uCDs/vsNHYbR0XliYWdnh9rahnK1vr6+SEtLQ/fu3QEABQXND2JrTzbFJ+KrPw6orH/1467jWPz4BPTtGgSgoSDTyQvpOJScBplcjt5dAjCkV2dILC0xfVgUxCIRNuxJQElFw7REPw8XzJ04GFGhLKBljCrz05F19FflLZHCC4fhHNwbXSYtRMA9D6LkaqJKZU0AEFtKEDJ2Hi7/8bHKQmXVRdlI3/0Nyq6fR7f730B59gVc/P3DRku/l1xNxIXfViD8kXd1/v7aG2sra7w36z2s2bUGh1IPoV5eDxuJDUb2HAkvJy+s2b2myeOOXz6O1KxUdPPrpueIqbUq8/NxJS4OhRcuwNLaGv6DByPwnnsgtmJPcFN0nlgMGDAAhw8fRlhYGMaPH4+XXnoJKSkp2LRpEwYMGKDr05uEi5l5WPV74/LPNXUyLF+3DT/FzoGVpQUWf7MFpy7+821o18lU/Lz7JFY+cx/cnOxw39DemDS4J67mFEBiaYEgH7dG5Zfzi8sRn3QJ1bV1CAvyRVRoQIslmkn7assKcPanNyGvqVTZXnI1Eec2xKLXnM/Q87EPkXPyTxSkHmookBUYjg79p0BWU6l29dOii0dRcjURN5J2NkoqbilNP4OKvDTY876+1jnbOeOVKa9g3th5KK0shYuDC6ytrPHmT282e9yxS8eYWBipgtRUHHznHchuG0txIzkZ6fv3Y8gbb8BCykKEd9J5YvHxxx+j4u+Rs0uWLEFFRQU2btyIkJCQVhfSAoBVq1Zh1apVSE9PBwB0794dixcvxtix+p+nr21bj6if0VFVW4c9py6goLRCJam4JfNGET79ZTfenjsZAGBlaYHQgKanQn2/4xh+2HlcZSBnZz9PLJ87Ga6Odm18F6SJ3FN/NUoqbqnMS0Nx2im4hvRBwJCHETBEtYBY2o6vmn3tm+cOoDz7YrP7lGdfZGKhQ7ZSW9jeVh69Ha2cYFYEhQInvvhCJam4peD8eVzauhXd7rvPAJEZN53XsXj77bdx8+ZNCIIAW1tbfPnll0hOTsamTZsQGNj6Lno/Pz+89957SEhIQEJCAoYPH47Jkyfj3LlzOoxeP3IKSpptzy4owfZjZ9W2n0hNR36x+oF+ABCfdAnr4441mh1yOSsf736/vdWxkna0tAJpWWbD/+va0pvIPv47rh/+RXmMooUl1RX1NbBoYc0PrgmiX31D+rapnQzj5vnzqGym2FX6vn16jKbB7ldfxdannsLuV1/V+7lbS+c9FoWFhRg/fjzc3Nzw4IMPYtasWeh1F1NxJk6cqPL8nXfewapVq3Ds2DHlmA1T5eXqCKRlq213dbBVWXzsTgpBwI2iMlTX1ePXfadw+lImLC0tMDg8BPcNjYSLgx02H0hSe/yZK1m4mnMTwb7tu6iLPllYNd99KraSIn3fd8g68qvKLQ2nwHC4dR0ENLFY1S2OAd1h590JGfu+a/rcEhu4dul/d4HTXRnZcyS2nNyCvJLGFRsjO0aiR0APA0RFLakpKWlTuy7UlJSguqhI7+fVhM57LLZs2YK8vDzExsbi1KlTiIqKQlhYGN59913lbQ1NyeVybNiwAZWVlYiOVl/6tra2FmVlZcpHhZpiJoY2YaD6mvLWEkuM7t8djnbql8IWiYCSiio89/HPiDt+DjeKy5F9swQb9ybguU824GZJOdJbKAt+LVd9iWnSPvdu9zTTKoJIbIGsw780GidRmpGCkmunIXVq+naXxMENnhEj4Nt3IuyavNUhQsdRT6kslka6Z2dth5WPrkR0aDTEf1dgtbayxvio8XjrgbcMHB2p4+TfeBbe7RwDAlBy7RqOffIJ/nj8cfz55JM4tXp1s70c7YFeSjE6OzvjqaeewlNPPYWsrCz8/PPP+Pbbb7F48WLIZLJWv05KSgqio6NRU1MDe3t7bN68GWFhYWr3X7FiBZYuXaqNt6BTYUG+eHzcQKy9owCWlaUFFs0aC2d7W4zt30Nt9c2+XYPw/Y7jqKmrb9SWX1yOdduPwtnBVu1CZkDLZcNJuzx6DMWNM7tQdr3xrTzffpNQcD5e7bFFl0+ixyPvImPvWpWxFHbenRA65WXlbY7wWe8h9+QW5Kfsg6ymAva+XdCh/xQ4B/XU/huiFrk7uuOt6W+htLIUJVUl8HD0UBmHQcbHKTAQHt2746aaW+5e4eHY88YbUNxWUfrqzp3IPnYMw955Bw4+Ta+MbO70WuO5vr4eCQkJOH78ONLT0+HVQr31O4WGhiIpKQklJSX47bffMHv2bMTHx6tNLhYtWoSFCxcqnyclJSEmJqZN70GbbhSVQSZXwNfdCQ+P7If+YR0Rd/wcCv+uYzFuQA94ujTUG5g1egAuXr+BpMuq1Rr9PFwwLSYSr32lvgT0/tMX8fCIfli3/WiT7Z4uDk3Wx2ita7kFOHL2KhQKBfp0DUS3wPZ5MWlCbGmF7g8vQ9aR35B/ZhfqKopg6xEAnz4T4dVrFA6/M1H9wYICirpq9Hz8Y1TkpaGmOA9SJ084+HZW2c1Sagv/wQ/Cf/CDOn43pAknOyc42TkZOgxqpf4vvIBD77yDktt72MVidJ0yBTkJCSpJxS21ZWU4+9NPiH7pJf0FakT0kljs27cPP/30E3777TfI5XJMmzYNf/75J4YPH67R60gkEoT8XdWsT58+OHnyJD777DP897//bXJ/qVQK6W1Tgezt7e/+TWjRyQvp+HbrYVzJbqi66OvuhEdGDcDIvt0wb9rQJo+RSizx/tPTcPz8NRxKuQKZTI7eoQEYFhmK1IzmV9qrq5dj/MBwnLqUiZQ7xnJYS6zwykOjIBY3PeU0v7gMu06moqSiGh193DCsd1fYSBvmbsvlCnzw807sOXVBuf/6uGPo1y0Ibz02vtlqn7cKf91eAKy9sbCyRmDMTATGzGzUZmXriPqqUrXHWto4AgDsvTtxdgeRFshqa1FXVgapkxMsJBLldhsXF4z44APcSEpCwa06FgMHQlZXhwubNql9veyTJyGvq1N5rfZC54mFn58fCgsLMXr0aPz3v//FxIkTYW2tfryAJgRBUBbfMhWnL1/HW2u2QK745955TkEpVv60A3KFAmP6/zMQVRAE1NTVQ2plBbFYBLFYhOgewYjuEazymh193CGxskBdvbzJcwZ4ucLZ3hbvPT0VexIuYF/iRVTV1qF7kC8m39MTvu7OTR73+4EkrPojXmUmydptR7B87mSEBnjjp10nVJKKW06kpuO/fxzA89PVr9b45UsPq20jwLPnCGQf/a3JNhs3Pzj6ddVzRETmqa6yEik//ICMAwcgr62Fpa0tgmJiED5zJiz//l0lEongHRkJ78hI5XGFly41+7qCTMbEQlcWL16M6dOnw8Wl+YWyWvL6669j7Nix8Pf3R3l5OTZs2ID9+/cjLi5OS5Hqx/rtR1WSCpW2uKMY2bcbFAoBP+8+gb+OpKCovAqOdtYY0787Zo0e0GQvgKOdNUb3644/Dyc3+brTh0UBACSWlhg7oAfGDmgYgV5XL8O+0xfx064TkFhZIqZXF/QM8QMAnE/PwZe/78ed0+9LKqqx+OstWPfGY/jzSNPnAxqKdz05YTDsbFg8pjWqbmaiqjALUgc3OHQIhf+gGSi5ehqVN66q7GchsUGnsc8h7/QO3Dy7D7LaKjh26AqfvhNh6373t7OI2iNFfT0OLFuG4rQ05TZZVRWubN+O0owMxCxZApFYDHl9Pa4fPozsEyegkMngFR4Ov4EDYWljA1l10zP2HPz8IDGSXnJ903li8dRTT2nldW7cuIFZs2YhNzcXTk5OiIiIQFxcHEaa0PK0ldW1OHstR237zZIKpGXfxA87j+Po2X9+oZRV1uCXvaeQmpGHD565DxYWjSfzPD1lCKpq6rA38YIyGbCytMBDI/qq9ILckldUile/3ITcwn+62/88nIwhPTvj9VljseVQcqOk4pai8irsOnkexeVVat9Lbb0MOQWl6OzvqXYfAmrLC3Hpj49Qmn5Guc3WMwhdJi1ExOyVyEvaiYLzB6Gor4VTYDi8e49B2vYvUZrxT1G1yrw03DizC92mvwWXTlwx2JgdvnAYO5N2oqiiCH5ufpjQZwK6+5v2dHlTlnXsmEpScbub588jLykJ7t264cCyZSi6fFnZlpeYiEtbtyJg0CBc3b27yeNDJ03SScymQK+DN9vim2++MXQIenHp+g2VpOJ2KWnZOHI2Dff07NyoTWJpidceGYNHxwzA6UvXYWkpRr+uHXE19yZ2nDgHf08XhAX5Kvd//4cdKknFLQfOXEbXQG9czy9uNs6bJRWwsrRAvazp2y8iEeDsoH5K47Mf/YTi8iq4ONi229sigkKOcz+9haqbGSrbq/LTcfanN9H7/1ahQ7/J6NBvsrIt6+hvKknFLQpZHS7/+Qn6Llhn8GXTJfYuKn9Sg0+3foqdSTuVz9Py0nDg3AE8PfppTOzbzGBd0pmchKZn2t3efiM5WSWpuKW6sBBlOTnoNGYMru7eDeHvGY4W1tYIu+8+dBw+HBW5ubh+5AhkNTVw79YN3pGR7WIJBZNJLMyBnY0UPTr6qu218HB2wLWc5utNHEy+okwsKmtqUV1bD1cHO+XgS193Z/i6O+N8eg4WfLYBeUVlymNDOnjgrcfGo65e3mzPydYjKQjydsWl62p3gberE2J6dcHuhNQm2yM7B8DDWf0KmsXlVSgoNc66IvpSeOl4o6TiFllVGfIS4xBwj+qMjvwzTX87AoC6iqKGUuCdDbuaba85nxn0/Mbo5JWTKknFLQIErN61GtGh0XB3dDdAZO1bS6XWBbm82eqaBefPo++zzyLsvvuQf+4cxBYW8OrZE1a2tkj+/ntc3LIFyq7fzZvh3LEj7nn9dVi3cWiAsdN5gSxSNXtsNCzETf+zzx47QO34i1tkMjmy8osR+80WTHvjKzy05GvMWv4tNh84rdynoLQCr//3d5WkAgCuZN/Ea19txvX85qu25RaWKMdhNMVGaoWhvbtg7sTB6ODh3Kjd1dEO8+8f1uw5CE3WsFBpz2xcxr2uqqTZY+or1c8kIcPZnaw+IZQr5Nh3Vv+loQnwuW0wZlM8evRAfWXTa/rcUl1YCGsXFwQMHgy/6GhY2doiff9+XPzjD9x5P7nk2jUc//zzNsdt7JhY6Fmvzv5456nJ6Oz3z9iDDh7OeO2RMRjdr3uL9SRC/Dzw4hf/+7tuRMN/2vzicny5OR7f/HUYAPDXkRS1xbByC0uR3cLaJF4ujhjQPRiTBjcupGRlYYFXHx4NO2spXB3t8J8XH8JTk+5BeHAHhAX5YPbYaHz18kz4eZh3Rq4NFpYtl/W+k51nx2aPsfMKaktIpCOlLSR8JZUl+gmEVPgPGgRHNdU1XUJC4B8dDWtXV7XHi8Ri2DdRBOvKtm1qj8lPSUHZ9Wa6g80Ab4UYQFRoIKJCA5FfXA65XAFvN0flfbfBESEI8nFDehMltr1dHXGzpAIlFU0PmvxtXyLui4lEakZus+cvLK1E10BvXFBT/2JcdENvxfz7hmFwRAh2HD+H4vIqdPR1x8SBESq9FHY2UkwfFqWceUKt5959CK4f3qi23S00GjkntqDg/EHIZQ2DNz3ChqgM9Lydo3932Ps0Hn9DhhfkGYTkDPWzqIK9gtW2ke5YSCSIWbIEp7/5BtnHj0OQyyG2soL/wIHo9cQTEFtaImT0aJz9+ecmj/ft1w+CQoG806chdXKCS3DD51ialdXseUuvX1eb0JgDJhYGdKuq5u0sLSyw8plp+HjjHpw4fw2Kv7vSIjv7Y+GDI/Dyv39V+3r1cjlOpqbD1rr5b8J21hL8a+ZovPrlb7hZojrOYUBYR5UkIbKzPyLbUJWT1LPzDIJ31DjknWr87cYxoAeyT/yBqtumm1bmpcFCagvvqPG4cXoHBMU/5fDtvDuh67TX9BI3NU+ukEMsEqsM0pvQZwK2JW6DTN54CQNXe1fcE9bc2jFt5/L3QFoXDqhtxNrJCdELF6K2rAzVRUWwdXdXmSYaOmUKSjIykHVEdckFp6AgyOvr8dezzwJ/38J2CghAn2efhbWzM6qaWS/ExszHWDCxMEIuDnZ4+8lJyC8uR25hKTydHeDj3lACWNHCYCO5QsDw3qE4eKbxKOZbhkd1hZ+HC77+16PYnZCK5LQsSCVWGNKzM/p2DVJbhZO0r9OYZ2HnGYzcU1tRXXAdEgd3ePUaBVlNOXKO/95of3ltFUrTz6Dv/LW4mXoQ8r/rWDh17NUuRpsbs6RrSdh4eCOS05NhIbZAdNdozLxnJgI8AuDn5od/Tf0XPt7yMarr/ql74O7gjtgZsZBY6raI0udzzP++fltJHR0hdXRstF1sYYHohQtROGECso8fh0Img2d4OC7+8QfyTp1S2bc0MxMHli1Dx3vvxaU//2zyPPa+vnDrat4F7phYGDFPF4dGvRp9ugZh+7HGg/oAwEIsRp/QQLg62mFgj2AcaWLa6vRhUQjwarhnaGstwaTBPZscS0H6IRKJ4BM1Fj5RY1W2H/9Y/RTc6sIs1Jblq0xDJcM6cvEI3v31XSj+Xo1WppDh4PmDSExLxAezP0CQZxAGdR2EyI6ROHj+IG6W3USQZxAGdBkASwv+GDYFbl26wK1LFwANNS4KUpueEVdfVQVBEOAeFoaC8+dV2qxsbdHvuefM/ksA/0ebmAeGRSE+6RKqmhicOaJPV+xKOI/Tl67DwkKMYZFdkJ5XhKKySvh5umDS4AgM723embI5EAQB9VVlze5Tx9kfRkMhKPD1rq+VScXtKmsr8UP8D3hz+puorqvGhkMbsDNpJ8qqy+Dt7I2CsgJM6jdJuZQ6mQZ1q53eUpCaiuHLlyPz0CFkHj4M+d91LDqNHg1b93+mFddXVyPv9GnI6+rg3rUr7L29dR26XjCxMDF+ni5Y+cx9+HLzfpxPbxikaW8jxdDILjickobiE6oDOzv7eeK7Nx5jaW0TIhKJYOsZhKr8a2p2EMPOM0ivMZF6V3KvIK9E/UKAxy4dQ1VNFd78+U1cyP5nbZ28kjys3rUa6fnpeGHiC3qIlNTRdE2Plva1kEggtrJC0LBhCBrW9NT7K3FxSPnxx39KgotE8B80CH2feQYWUtP+ec3EwgSFBnjhs+dnILewFJU1tfDzcMGirzY3WWL7clY+vos7imenDtV7nHT3OvSfjMt/ftpkm1uX/rB29tJvQKRWvay+2XaFoMDes3tVkorb7TyzE5P7TUZHr+anEpN2KerrceH335G2axdqiopg7eyM4JEj0XXaNFhYqV+ZGQA6DBiA5B9/VA7avJNfdHSzx2efOIHTX3+tulEQcP3QIYgtLdHvuec0ei/Ghv1vRqiwtLJVVSl93JwQ0sETBSUVzVbS3HnifIuFt8i4ePUcCb+B04E7usgdA8LRecLzBoqKmtLJuxPspHZq27v4dsHJKyebfY1DqYe0HRY1QxAEHPnoI5zbuBE1RQ0FA2tKSnD+f//D4fffh9DCz0t7Ly90GT++yTbnoCB0HD682eMvbtmiti3z4EFUFzVfxNDYscfCiJxMTcfabUdwOathmlKwrztmj4nGwPBOjfatrKmF1MoSlhYWKCxrvjJcZU0dautksLVuf8v3mrKg4Y/BO2ocCi8c/nsRsgg4+ocZOiy6g7XEGlP6T8GPB35ssn3GoBnYclL9LxIAqJM1XdCOdCM/ORm5atYJuZGUhLzTp+ET1Xxtnp6zZ8PB1xeX//oLZVlZkNjbI2jYMHS7/37lcuvqFDWz5Logl6M4LQ02zRTmMnZMLIzEidRreOvrLcpqmgBwNacAS9b+ibcem4B7IkIAAH8cOoNN8aeRU1ACqZUlhkZ2weTBvSAWi1SOvZ2HswNspM137ZFxsnbyRIf+Uw0dBrXg4XsehkKhwO8nfldOJ3Wxd8Hjwx5HdGg0Mm5m4IyawmYA0LMjZ2bpU9axYy22t5RYAEDwyJEIHjkSglwOkUXTi/8VXbmCnJMnIQgCvHv1gkdYGCxtbVFfob5X2tJG/QKOpoCJhZFYt+1ok4mBIADrth3BPREhWLPlIH7Z98+86dp6GXacOI/ktGwMCOvY5PRSAJg0OMLspzcRGZJIJMKsobNwX/R9uJB9AVYWVujm1005lXRs5FhsObmlydLdXXy7ICqYlWv1SV7f/LgYeV0dasvKcGX79obaFXI5vCIi0Hn8+CZnbjSVVCjq63Hss8+QfVsSc2HTJnhFRMA/OhpXd+1q8ty27u7w6NZNw3dkXDjGwggUlFYob380JfNGEc5ezcav8YlNtucWlsLfyxVhQY1r1o/o043lton0xFZqi97BvREeGK5Sn8LJzgkrHlmBLr5dlNvEIjEGdBmApQ8uZeKvZ5491C+yCADOHTti92uv4fz//ofSzEyUZ2fjyvbt2P3qqyi6ckVlX1ltLcqys1FbqjoF/Nwvv6gkFbfcSE6GrLYWdl6NB2CLLC0R+eSTans/TAV7LIxB88U0AQCJlzLV3uoAGsZnfPXyTCReykTipUxYWVhgcM8QhHTwVNnvzJXr2HokBTkFpfBydcSE6HD0Dg1o6zvQmIuDrcqfROYu0CMQnz7xKdLz01FUXoQObh3gxdk9BuE/cCBSf/sNFbmN11Wy8/JCSUZGkyW566uqkLhmDUa8/z4U9fVI+eknXN2zB7KqKkAshk/v3oh8/HHYuLri6m71K9pmHzuGUR99hKt79uD6kSOQ19bCIywMoZMnwzUkRKvv1RCYWBgBd2d7dPRxx7Xcgibbfd2dYdfC+h8yuQIikQghHTxRU1cPSwsL+HuoDv75cedxrNt+VPn80vUbOHjmMh4e2Q+PjxvY9jeigS9fUl9ZksicBXkGIYh1SAzq1uJjJ//zH+Qn/7M4nEePHoh66insfPlltccWp6WhLCsL5zZuRNbRf36eQqFAbkICSq5dw+BFi1BXXq72NeR1dZDV1iLikUcQ8cgjWnlPxoSJhZGYPXYAlq7diqaWApk9ZgCCfT2aPT6yiz+++j0eWw4no14mBwA42Erx+LiBmDioJ9JzC1WSitv9tOsEBoeHoLO/Z5PtRETmxtbNDTGLF6MiNxeV+fmw9fSEg48P6ioroahrfpZO0eXLqknFbaoLC5F17BjEEon61xGLYe3kdFdxWzs7q/xpjJhYGIlB4SF489HxWLf9CK7nFwNo6KmYPWYAhkc1lOEeHBGCQ8lXGh1rZy2BXK7AHwdVR52XV9Xi81/3wdHOBhczbzR7/h0nzjGxIKJ2x97HB/Y+/4xPs7K1hZ23Nyrzmq6mKpZIUN7ELZTb3ThzBv7R0ciIj2+y3adXL1jf5QqnI1auvKvj9ImJhREZ0qszhvTqjKz8YggQ4OfhojKo67WZY7DKbj92nkxV9kp09vPE/026B299o36e/IbdJxHk4662HUCTVTuJiNobkUiELuPH4/Q33zTZHjR0aIt1KiASIeLRR1F05QrKs7NVmmzd3RH55JPaCtcoMbEwQn6eTWeyUoklXnhgBJ4YPwiZN4rgYGeDQC9XJKdlobpW/fSpK9k3MTii+QFBHX2bTzyIiNqLkLFjUVVQgEtbt0KQy5Xb/aKj0euxx1CRm4uzP/2k9njfqChYOzlhxHvvIX3/fmSfPAlBoYBPZCQ63nsvJPb2+ngbBsPEwgQ52tmgR3AH5XNrSfPFrywtxBjdvzt+2XsKVbWN7/lZSywxtn/z06+IiNqTiFmz0HncOOQkJEAhk8GrZ084+vkBAJwCA+E/eDCuH2pcit3W3R3Bo0YBaCh0FTJ2LELGjtVaXLtffRU1JSWwdnY22tsiTCzMQGc/T3TwcEb2zZIm2wdHhMDdyR7LnpyIpWv/QnlVjbLN3kaKNx4dBzcn9WsdEJF2nEo7hR1JO1BUXgR/d39M6DMBnbwbl+wn42Dj5oZOo0c32dbvuedg5+GBq7t2oa6iAiILC/j26YNejz0GqYODzmKqKSkx+rVEmFiYAZFIhGenxiD2mz8hk6sunuNkZ4PZYxpW2usZ4o+fYufgQNJl5BY21LGI6dWF5b6J9OCrHV+prBlyPus8dp3ZhQXjF2BUr1EGjIzuhtjSEuEzZyLsgQdQXVgIib292d/iaC0mFmaiX7eO+Oi5+/Hz7gScvpQJSwsx7unZGQ+N6Atfd2flftYSK4zqx4WsiPQp8WpikwuRKQQF/r3t3+gT0geu9qa76JS5Ks/JwYXNm5F98iSgUMA7MhJdp06Fc1CQch8LK6smy3y3Z0wszEhYkC/efnKSocMgojvsSmp6XQgAkClk2JeyD/dF36fHiKglpRkZ2Ld4Meor/1k9+vrhw8hJSMCQt96Ce9euBozOuHGtECIiHSuuLG62vajCuO+Zt0fJ33+vklTcIq+txZnvvjNARKaDiQURkY75u/s32x7oEainSKg16ioqkHdG/TL3RZcvo/JG80UH2zOTSSxWrFiBvn37wsHBAZ6enpgyZQouXrxo6LCIiFo0oc8EWIibXrHSydYJQ7oP0XNE1BxZbS2aXF/h9n1qanDz3Dkc+eADbJ8/H3vffBNXd++G4ra6F+2VySQW8fHxmDdvHo4dO4Zdu3ZBJpNh1KhRqGyiq4qIyJgEegRi4aSFkFhKVLY72zljyYwlsLZqoZIj6ZWNiwvsPNUvcSB1dET+uXPYv2QJso8fR0VuLgovXMCpr77CkQ8+aPfJhckM3oyLi1N5vnbtWnh6euLUqVMYMoTZPhEZt2E9hiEqOAr7zu5DUUUR/N38cU/YPZBaNb9yMemfSCxG6OTJSFyzpsn2jiNGIHn9+iZ7NXITEnD98GEEtuPfSyaTWNyptLQUAODqyilaRGQaHG0dMbnfZEOHQa3QafRo1FVU4MLmzZDVNBQVFEsk6DJ+PKydnaGQydQem7F/PxMLUyMIAhYuXIjBgwejRw/1pahra2tRW1urfF5RUaGP8IiIyAx0u+8+hIwdi/yUFAiCAM/u3SFxcMC5X35p9ri6dv67xiQTi+eeew7Jyck41ESd9tutWLECS5cu1VNURERkbqxsbdGhf3+VbS7Bwc0e49xCu7kzmcGbt8yfPx9btmzBvn374Pf3gjDqLFq0CKWlpcpHfHy8nqIkIiJz5dO7Nxw6dGiyTWRpic5aXHTMFJlMYiEIAp577jls2rQJe/fuRceOHVs8RiqVwtHRUfmwZx13IiJqI5FYjMGvvw7HgACV7Va2thjwwgtwCmzfdUlM5lbIvHnz8NNPP+GPP/6Ag4MD8vLyAABOTk6wsbExcHRERNSe2Ht5YdRHHyE/JQWlGRmQOjmhQ//+sJRylo/JJBarVq0CAAwdOlRl+9q1a/HYY4/pPyAiImrXRCIRvCIi4BURYehQjIrJJBZCC1XQiIiIyPBMZowFERERGT+T6bEgIiLSp8r8fFyJi0PB+fOwkEjgN3AggoYN4ziKFjCxICIiukPhpUs4sHw5ZFVVym03z59H+r59iFmyBFacNKAWb4UQERHd4eR//qOSVNxSnJaGC5s2GSAi08HEgoiI6DaFly6hPDtbbXv6/v0qz+W1tRAUCh1HZTp4K4SIiOg2tX8vctlcuyAIuLJ9O65s24aKvDxY2toiKCYG3R94ABIHBz1FapyYWBAREd3GMSAAEImaXBYdAJwCApD07be4sn27cpusqgpXtm9H/rlzGP7OO+16DAZvhRAREd3G3ssLPr17q233HzQIV+Limmwry8xE+t69ugrNJDCxICIiukPf556DW2io6kaxGKGTJzfbmwEAWUeP6iwua2dn2Li6wtrZWWfnaCveCiEiIrqD1MEBw995BzfPncPNW3UsoqNh5+mJ8//7X7PHymUyncU1YuVKnb22tjCxICIiUsOje3d4dO+uss0zPBznNm5Ue4xXeLiuwzJqvBVCRESkAfeuXeGpJnmQ2Nuj05gxeo7IuDCxICIi0tDAV15BwODBEFlYKLe5BAcjJjYWtm5uBozM8HgrhIiISENWtrbo/8ILCJ0yBTeSk2Hv7Y0O/foZOiyjwMSCiIhIQ/XV1UhcvRrXjxyBIJcDAFxCQtDn//4Pzh07Gjg6w+KtECIiIg0d+eADZB48qEwqAKD4yhXEL12KqsJCA0ZmeEwsiIiINFB48SLyk5ObbKurqECamuJZ7QUTCyIiIg3cSElpU7u5Y2JBRESkAbFl88MTW2o3d0wsiIiINODXv39DWW917QMG6DEa48PEgoiISAP2Pj4IUVMEy9HfHx3vvVfPERmX9t1fQ0REdBd6PfEE7L29cXnbNlTeuAFLGxsEDhmC7g8+2K6XTAeYWBAREWlMJBKh8/jx6Dx+PGQ1NbCQSCAS8yYAwMSCiIioTSytrQ0dglFhYkFERHQHQaFATkICrh8+DFl1NdxCQ9FxxAhYOzkZOjSjx8SCiIjoNgq5HEc/+AA5CQnKbbmJibi0dSuGvPkmXDp1MmB0xo83hIiIiG5zZft2laTilrrychz77DMIggAAkNfXI/PgQST/8AMu/fknakpK9BypcWKPBRER0W2u7d6ttq0iJwcFqamwsrXFoXffRXVRkbIt+ccf0XvOHASPHKmPMI0WeyyIiIhuU3VbstCUyvx8HH7vPZWkAgAEmQynVq9G0eXLugzP6JlUYnHgwAFMnDgRvr6+EIlE+P333w0dEhERmRkHH59m26uLilBVUNB0oyDgyvbtOojKdJhUYlFZWYmePXvi3//+t6FDISIiM9Vp9Gi1bS4hIVDIZM0eX5qVpe2QTIpJjbEYO3Ysxo4da+gwiIjIjHUcPhzFaWlI27FDZbudlxcGvPgibpw50+zxNs7OOozO+JlUYqGp2tpa1NbWKp9XVFQYMBoiIjIVvefORfDIkcg8dEhZx8I/OhpiKytIBg3CmfXrIa+pafLYoOHD9RytcTHrxGLFihVYunSpocMgIiIT5BwUBOegoEbbJXZ26PvMMzj++ecQ5HKVtqBhw9Chf389RWicRMKtCbkmRiQSYfPmzZgyZYrafe7ssUhKSkJMTAxOnTqF3r176yFKIiIyV6XXryMtLg4lGRmwdnRE0PDh8O3Tx9BhGZxZ91hIpVJIpVLlc3t7ewNGQ0RE5sTJ3x+95841dBhGx6RmhRAREZFxM6kei4qKCly5ckX5/Nq1a0hKSoKrqysCAgIMGBkREREBJpZYJCQkYNiwYcrnCxcuBADMnj0b69atM1BUREREdItJJRZDhw6FiY411bvc3Fzk5uYaOgzSEh8fH/i0UA2QTAevT/PDa/QfJpVYtJWPjw9iY2PN/sOvra3FQw89hPj4eEOHQloSExODHTt2qAxGJtPE69M88Rr9h8lONyX1ysrK4OTkhPj4eM6EMQMVFRWIiYlBaWkpHB0dDR0OtRGvT/PDa1RVu+qxaG969erF/+RmoKyszNAhkA7w+jQfvEZVcbopERERaQ0TCyIiItIaJhZmSCqVIjY2loOIzAQ/T/PCz9P88DNVxcGbREREpDXssSAiIiKtYWJBREREWsPEgoiIiLSGiQU1sn//fohEIpSUlBg6FCJqAq9RMmZMLHQsLy8P8+fPR3BwMKRSKfz9/TFx4kTs2bNHq+cZOnQoXnjhBa2+ZnNWr16NoUOHwtHRkT/gmiASiZp9PPbYY3f92kFBQfj0009b3I+fUeuY4zVaVFSE+fPnIzQ0FLa2tggICMCCBQtQWlqql/MbO0Nfn+b++bDypg6lp6dj0KBBcHZ2xsqVKxEREYH6+nrs2LED8+bNw4ULF/QajyAIkMvlsLRs+8deVVWFMWPGYMyYMVi0aJEWojMvty8wtXHjRixevBgXL15UbrOxsdF5DPyMWmau12hOTg5ycnLw4YcfIiwsDBkZGXj66aeRk5ODX3/9VUvRmi5DX59m//kIpDNjx44VOnToIFRUVDRqKy4uVv49IyNDmDRpkmBnZyc4ODgI06dPF/Ly8pTtsbGxQs+ePYX169cLgYGBgqOjozBjxgyhrKxMEARBmD17tgBA5XHt2jVh3759AgAhLi5OiIqKEqysrIS9e/cKNTU1wvz58wUPDw9BKpUKgwYNEk6cOKE8363jbo9RHU32ba/Wrl0rODk5qWzbsmWL0Lt3b0EqlQodO3YUlixZItTX1yvbY2NjBX9/f0EikQg+Pj7C/PnzBUEQhJiYmEafdUv4GanXHq7RW3755RdBIpGo/D8jw1+ft5jT58PEQkcKCwsFkUgkvPvuu83up1AohMjISGHw4MFCQkKCcOzYMaF3795CTEyMcp/Y2FjB3t5emDZtmpCSkiIcOHBA8Pb2Fl5//XVBEAShpKREiI6OFubOnSvk5uYKubm5gkwmU/7wiYiIEHbu3ClcuXJFKCgoEBYsWCD4+voK27ZtE86dOyfMnj1bcHFxEQoLCwVBYGKhbXf+4IqLixMcHR2FdevWCWlpacLOnTuFoKAgYcmSJYIgCML//vc/wdHRUdi2bZuQkZEhHD9+XFi9erUgCA3/r/z8/IRly5YpP+uW8DNqWnu5Rm9Zs2aN4O7urvG/k7kz9PV5izl9PkwsdOT48eMCAGHTpk3N7rdz507BwsJCyMzMVG47d+6cAED5DSU2NlawtbVVfvsRBEF45ZVXhP79+yufx8TECM8//7zKa9/64fP7778rt1VUVAhWVlbCjz/+qNxWV1cn+Pr6CitXrlQ5jomFdtz5g+uee+5p9Mvs+++/F3x8fARBEISPPvpI6NKli1BXV9fk6wUGBgqffPJJq8/Pz6hp7eUaFQRBKCgoEAICAoQ33nijVfu3J4a+PgXB/D4fDt7UEeHvgqYikajZ/VJTU+Hv7w9/f3/ltrCwMDg7OyM1NVW5LSgoCA4ODsrnPj4+yM/Pb1Usffr0Uf49LS0N9fX1GDRokHKblZUV+vXrp3I+0p1Tp05h2bJlsLe3Vz7mzp2L3NxcVFVVYfr06aiurkZwcDDmzp2LzZs3QyaTGTpss9NertGysjKMHz8eYWFhiI2N1fj49kbf16c5fj5MLHSkc+fOEIlELf4gEAShyR9sd263srJSaReJRFAoFK2Kxc7OTuV1bx3fmjhI+xQKBZYuXYqkpCTlIyUlBZcvX4a1tTX8/f1x8eJF/Oc//4GNjQ2effZZDBkyBPX19YYO3ay0h2u0vLwcY8aMgb29PTZv3twoRmpMn9enuX4+TCx0xNXVFaNHj8Z//vMfVFZWNmq/NfUvLCwMmZmZuH79urLt/PnzKC0tRbdu3Vp9PolEArlc3uJ+ISEhkEgkOHTokHJbfX09EhISNDof3b3evXvj4sWLCAkJafQQixsuSRsbG0yaNAmff/459u/fj6NHjyIlJQVA6z9rap65X6NlZWUYNWoUJBIJtmzZAmtr61Yf257p6/o058+H00116Msvv8TAgQPRr18/LFu2DBEREZDJZNi1axdWrVqF1NRUjBgxAhEREZg5cyY+/fRTyGQyPPvss4iJiVHpHm1JUFAQjh8/jvT0dNjb28PV1bXJ/ezs7PDMM8/glVdegaurKwICArBy5UpUVVVhzpw5rT5fXl4e8vLycOXKFQBASkoKHBwcEBAQoPbc1GDx4sWYMGEC/P39MX36dIjFYiQnJyMlJQXLly/HunXrIJfL0b9/f9ja2uL777+HjY0NAgMDATR81gcOHMCDDz4IqVQKd3f3Js/Dz6hl5nqNlpeXY9SoUaiqqsIPP/yAsrIylJWVAQA8PDxgYWHR6rjbG31cn2b/+RhqcEd7kZOTI8ybN08IDAwUJBKJ0KFDB2HSpEnCvn37lPu0dirb7T755BMhMDBQ+fzixYvCgAEDBBsbm0ZT2e4c4FVdXS3Mnz9fcHd3v+upbLGxsY2mVQEQ1q5dexf/SuatqelscXFxwsCBAwUbGxvB0dFR6Nevn3Jk+ebNm4X+/fsLjo6Ogp2dnTBgwABh9+7dymOPHj0qRERECFKptNnpbPyMWsccr9Fb7U09rl27dpf/UubJENenuX8+XDadiIiItIZjLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJhQE99thjEIlEeO+991S2//777zqtgllfX49//etfCA8Ph52dHXx9ffHoo48iJydHZb/a2lrMnz8f7u7usLOzw6RJk5CVlaWzuEwdP0/zws/TvPDz1B8mFgZmbW2N999/H8XFxXo7Z1VVFRITE/HWW28hMTERmzZtwqVLlzBp0iSV/V544QVs3rwZGzZswKFDh1BRUYEJEyaw6mMz+HmaF36e5oWfp54YupBGezZ79mxhwoQJQteuXYVXXnlFuX3z5s3NFj7ShRMnTggAhIyMDEEQGpZ5trKyEjZs2KDcJzs7WxCLxUJcXJxeYzMV/DzNCz9P88LPU3/YY2FgFhYWePfdd/HFF19o1O01duxYldX3mnpoorS0FCKRCM7OzgAaVvirr6/HqFGjlPv4+vqiR48eOHLkiEav3Z7w8zQv/DzNCz9P/eBaIUZg6tSp6NWrF2JjY/HNN9+06pivv/4a1dXVWjl/TU0NXnvtNTz88MNwdHQE0LDOhEQigYuLi8q+Xl5eyMvL08p5zRU/T/PCz9O88PPUPSYWRuL999/H8OHD8dJLL7Vq/w4dOmjlvPX19XjwwQehUCjw5Zdftri/wOXVW4Wfp3nh52le+HnqFm+FGIkhQ4Zg9OjReP3111u1vza65urr6/HAAw/g2rVr2LVrlzJ7BgBvb2/U1dU1GuSUn58PLy8vzd5cO8TP07zw8zQv/Dx1iz0WRuS9995Dr1690KVLlxb3bWvX3K3/5JcvX8a+ffvg5uam0h4VFQUrKyvs2rULDzzwAAAgNzcXZ8+excqVK+/6vO0JP0/zws/TvPDz1B0mFkYkPDwcM2fOxBdffNHivm3pmpPJZLj//vuRmJiIrVu3Qi6XK+/jubq6QiKRwMnJCXPmzMFLL70ENzc3uLq64uWXX0Z4eDhGjBhx1+duT/h5mhd+nuaFn6cOGXZSSvs2e/ZsYfLkySrb0tPTBalUqtPpT9euXRMANPnYt2+fcr/q6mrhueeeE1xdXQUbGxthwoQJQmZmps7iMnX8PM0LP0/zws9Tf0SCIAj6SWGIiIjI3HHwJhEREWkNEwsiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJBREREWkNEwsiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJBREREWkNEwsiIiLSGpNILNLT0zFnzhx07NgRNjY26NSpE2JjY1FXV2fo0IiIiOg2loYOoDUuXLgAhUKB//73vwgJCcHZs2cxd+5cVFZW4sMPPzR0eERERPQ3kSAIgqGDuBsffPABVq1ahatXrxo6FCIiIvqbSfRYNKW0tBSurq7N7lNbW4va2lqVbVKpFFKpVJehERERtVsmMcbiTmlpafjiiy/w9NNPN7vfihUr4OTkpPIYPXo0cnNz9RQpERG1B1nz5+Paffej6LvvDB2KwRk0sViyZAlEIlGzj4SEBJVjcnJyMGbMGEyfPh1PPvlks6+/aNEilJaWKh/x8fGIj49nYkFERDpREX8Agkxm6DAMyqC3Qp577jk8+OCDze4TFBSk/HtOTg6GDRuG6OhorF69usXXv/O2h729/V3HSkRE1BJ5aSmqTpyA3cCBhg7FYAyaWLi7u8Pd3b1V+2ZnZ2PYsGGIiorC2rVrIRab5F0cIiIyM3369EH2hQtwt7LCluH3ovTPre06sTCJ3845OTkYOnQo/P398eGHH+LmzZvIy8tDXl6eoUMjIqJ2Li8vD3mVlSioaZgsUHvpEqoSEw0cleGYxKyQnTt34sqVK7hy5Qr8/PxU2kx0tiwREZmxwm+/hXWPHhBLJIYORe9MosfisccegyAITT6IiIiMjSw3D0Vr1xk6DIMwicSCiIjI1JTv3ImK+HhDh6F3TCyIiIh0pOCr/6I2Lc3QYegVEwsiIiIdEerqcGPlSshLSgwdit4wsSAiItIheUEhbqz8AEI7WZGbiQUREZGO1V68iPxPP4Mglxs6FJ1jYkFERKQHVcePI//jj82+54KJBRERkZ5UHTuOvLffhry01NCh6AwTCyIiIj2qOZ+K7FdfRc3Fi4YORSeYWBAREemZvKAQuW8tRsmvv0JQKAwdjlYxsSAiIjIEuRzFP29A7uLFqL+Rb+hotIaJBRERkQHVpl5A9ksLUbZrl6FD0QomFkRERAYmVNeg8Kv/Iu/dd02+mFarVzdduHChynOxWAx7e3vY29vj5ZdfxnvvvYf8/Hx8/PHHWg+SiIioPag+lYjshS/B48UXYBMebuhw7kqrE4vTp0+rPL+VWDg7OwMAzp8/j6ysLK0GR0RE1N7IS0uRt+xtuM15Ao5jxhg6HI21OrHYt29fs+3r169vczBEREQEQKFA4ZqvIdTXw2niRENHoxGNx1hkZGSgurpaF7EQERHRbYq+W4+Kw4cNHYZGNEosFAoFOnfuzFseRERE+iAIKPjPl6i9ds3QkbSaRomFWCxG586dUVhYqKt4iIiITEZmZiaqqqoAAFVyGbL//rs2CbW1yH/fdJZe1/hWyMqVK/HKK6/g7NmzuoiHiIjI6J04cQITJ05EUFAQiouLAQBl9fUYErcdc48cwZmiIq2eT3bzJvLefRcKExiKIBIEQdDkABcXF1RVVUEmk0EikcDGxkalvUjL/5jalJiYiKioKJw6dQq9e/c2dDhERGSCNm3ahBkzZkAQBMibWAbdQiSCCMDn/fpjTIcOWj23dUQ4vN94AyLLVs+90DuNI/v00091EAYREZHxO3HiBGbMmAG5XA5138vlggARgAUnjuN/MUPR09VVa+evSU5B4dffwP3p/9Paa2qbxonF7NmzdREHERGR0Vu+fDkEQVCbVNwi/P34z8ULWB09UKsxlO/aBduo3rDt21err6std1XSOy0tDW+++SYeeugh5Oc3LJwSFxeHc+fOaTU4IiIiY5GZmYmtW7c2efujKXJBwJ7cXJ0M6CzesFHrr6ktGicW8fHxCA8Px/Hjx7Fp0yZUVFQAAJKTkxEbG6v1AImIiIzBnj17WuypuJMA4OhN7a9cWpeeDpmRztDUOLF47bXXsHz5cuzatQsSiUS5fdiwYTh69KhWgyMiIjIW5eXlEIs1+7UpBlBRL9NJPPLSMp28bltpnFikpKRg6tSpjbZ7eHiwvgUREZktBwcHKBQKjY5RALC30s0MDgsnR528bltpnFg4OzsjNze30fbTp0+jg5an1RARERmLe++9FyKRSKNjRACiPTy1HouFkxMs3dy0/rraoHFi8fDDD+Nf//oX8vLyIBKJoFAocPjwYbz88st49NFHdREjERGRwQUEBGDChAmwsLBo1f4WIhHu9fFBB1tbrcdibcRLqmucWLzzzjsICAhAhw4dUFFRgbCwMAwZMgQDBw7Em2++qYsYiYiIjMJbb70FkUjUYs+F6O/HvNCuOonDtnekTl5XGzS+8WNlZYUff/wRb7/9NhITE6FQKBAZGYnOnTvrIj4iIiKj0bdvX2zcuLFVlTe/6Ndfq8WxbhHZWMO2Xz+tv662aNxjsWzZMlRVVSE4OBj3338/HnjgAXTu3BnV1dVYtmyZLmIkIiIyGtOmTcORI0cwbty4Rj0XIgDDvL3xv5ihGK2jcYeO48ZBfMdyGsZE47VCLCwskJubC09P1cEohYWF8PT0bHXhEEPgWiFERKRNmZmZ6NWrF4qLi+FkZYWt947QyZiKWyQdO8L33Xcguq3cg7HRuMdCEIQm7y2dOXMGrjro8iEiIjJWAQEBsP07kbCxsNRpUmHVoQO83njdqJMKQIMxFi4uLsoBK126dFFJLuRyOSoqKvD000/rJEgiIqL2zDoiHJ4vvQQLe3tDh9KiVicWn376KQRBwBNPPIGlS5fCyclJ2SaRSBAUFITo6GidBElERNQuWVrA5YEH4DR1KkQaVv00lFYnFrdWNe3YsSMGDRoESyNeC56IiMjUSUM6we3ppyHt2NHQoWhE4+xg+PDhJjt4k4iIyNiJbWzgMvNhOIwebTK9FLfTOLFQN4mktrZWZVEyIiIi0oztgP5we/JJWLq4GDqUu9bqxOLzzz8HAIhEInz99dewv20AiVwux4EDB9C1q24qjBEREZkzsaMD3P/v/2A3YIChQ2mzVicWn3zyCYCGHouvvvpKpVb6rcGbX331lfYj/Ns777yDv/76C0lJSZBIJCgpKdHZuYiIiPTFOiwMHgtfNOleitu1OrG4du0aAGDYsGHYtGkTXPT8D1BXV4fp06cjOjoa33zzjV7PTUREpAsOI+6F21NPQdTKhc1MgcZjLPbt2weg4Rf9tWvX0KlTJ73MEFm6dCkAYN26dTo/FxERka45TZkC11mPGDoMrdN4uGl1dTXmzJkDW1tbdO/eHZmZmQCABQsW4L333tN6gG1RW1uLsrIy5aOiosLQIREREcHpvmlmmVQAd5FYvPbaazhz5gz2798Pa2tr5fYRI0Zg48aNWg2urVasWAEnJyflIyYmxtAhERFRO+d03zS4PvywocPQGY0Ti99//x3//ve/MXjwYJWy3mFhYUhLS9PotZYsWaIsE67ukZCQoGmISosWLUJpaanyER8ff9evRURE1FbOD84w66QCuIsxFjdv3mxUHAsAKisrm1ycrDnPPfccHnzwwWb3CQoK0ug1byeVSiGVSpXP7U2gxjoREZkhsRhujz8Gx3HjDB2JzmmcWPTt2xd//fUX5s+fDwDKZGLNmjUarxXi7u4Od3d3TUMgIiIyGSJLS7jPnw/7wYMMHYpeaJxYrFixAmPGjMH58+chk8nw2Wef4dy5czh69KhObzVkZmaiqKgImZmZkMvlSEpKAgCEhISwJ4KIiIySSCKB5yuvwLZ3pKFD0RuNx1gMHDgQhw8fRlVVFTp16oSdO3fCy8sLR48eRVRUlC5iBAAsXrwYkZGRiI2NRUVFBSIjIxEZGdmmMRhEREQ6Y2nR7pIK4C56LAAgPDwc3333nbZjada6detYw4KIiEyG2+OPt7ukArjLxEIul2Pz5s1ITU2FSCRCt27dMHnyZC6lTkRE7Y63tzfkJSVwt7JSbrPtEwXHMWMMGJXhaJwJnD17FpMnT0ZeXh5CQ0MBAJcuXYKHhwe2bNmC8PBwrQdJRERkrBISEpA1fz7qc3IBACKpFG5z5xo4KsPReIzFk08+ie7duyMrKwuJiYlITEzE9evXERERgaeeekoXMRIREZkMp6lTYNmOZzxq3GNx5swZJCQkqCxC5uLignfeeQd9+/bVanBERESmxMLNFU6TJxs6DIPSuMciNDQUN27caLQ9Pz8fISEhWgmKiIjIFDnfdz/EEomhwzCoViUWty/k9e6772LBggX49ddfkZWVhaysLPz666944YUX8P777+s6XiIiIqMktrODw7Chhg7D4Fp1K8TZ2VmlXLcgCHjggQeU2wRBAABMnDgRcrlcB2ESEREZN9s+fSBq570VQCsTi3379uk6DiIiIpNm3aO7oUMwCq1KLLjcOBERUfMkQR0NHYJR0HjwJhEREd1BbAGJXwdDR2EUmFgQERG1kZW3F8dX/I2JBRERURtZdfAzdAhGg4kFERFRG1n6eBs6BKOhcWJRXV2Nqqoq5fOMjAx8+umn2Llzp1YDIyIiMhWWbu23hPedNE4sJk+ejPXr1wMASkpK0L9/f3z00UeYPHkyVq1apfUAiYiIjJ2Fs5OhQzAaGicWiYmJuOeeewAAv/76K7y8vJCRkYH169fj888/13qARERExk5sa2voEIyGxolFVVUVHBwcAAA7d+7EtGnTIBaLMWDAAGRkZGg9QCIiImMnkkoNHYLR0DixCAkJwe+//47r169jx44dGDVqFICGRcgcHR21HiAREZGxE1lZGToEo6FxYrF48WK8/PLLCAoKQv/+/REdHQ2gofciMjJS6wESEREZOwt7e0OHYDRaVdL7dvfffz8GDx6M3Nxc9OzZU7n93nvvxdSpU7UaHBEREZkWjRMLAPD29oa3t+qc3X79+mklICIiIjJdrUospk2bhnXr1sHR0RHTpk1rdt9NmzZpJTAiIiIyPa1KLJycnCASiZR/JyIiImpKqxKLtWvXNvl3IiIiottxrRAiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1rQpsaipqdFWHERERGQGNE4sFAoF3n77bXTo0AH29va4evUqAOCtt97CN998o/UAiYiIyHRonFgsX74c69atw8qVKyGRSJTbw8PD8fXXX2s1OCIiIjItGicW69evx+rVqzFz5kxYWFgot0dERODChQtaDY6IiIhMi8aJRXZ2NkJCQhptVygUqK+v10pQREREZJo0Tiy6d++OgwcPNtr+v//9j8umExERtXMar24aGxuLWbNmITs7GwqFAps2bcLFixexfv16bN26VRcxEhERkYnQuMdi4sSJ2LhxI7Zt2waRSITFixcjNTUVf/75J0aOHKmLGImIiMhEaNxjAQCjR4/G6NGjtR0LEakjqwMsJS3vR0RkYBr3WDz++OPYs2cPBEHQRTxE1BSxRcv7EBEZAY0Ti8LCQowfPx5+fn546aWXcPr0aV3ERUS3E7H6PhGZBo1/Wm3ZsgV5eXmIjY3FqVOn0KdPH4SFheHdd99Fenq6DkJU9eWXX6Jjx46wtrZGVFRUkzNUiIiIyDDu6muQs7MznnrqKezfvx8ZGRl4/PHH8f333zdZ30KbNm7ciBdeeAFvvPEGTp8+jXvuuQdjx45FZmamTs9LZHC89UhEJqJN/av19fVISEjA8ePHkZ6eDi8vL23F1aSPP/4Yc+bMwZNPPolu3brh008/hb+/P1atWqXT8xIZnCA3dARERK1yV7NC9u3bh59++gm//fYb5HI5pk2bhj///BPDhw/XdnxKdXV1OHXqFF577TWV7aNGjcKRI0eaPKa2tha1tbXK5xUVFQAAmUzGKqFkWuqqAImtoaMgonbOysqqxX00Tiz8/PxQWFiI0aNH47///S8mTpwIa2vruwpQEwUFBZDL5Y16Rby8vJCXl9fkMStWrMDSpUsbbe/fv79OYiQiIjJnrZkRqnFisXjxYkyfPh0uLi53FVRbiUQileeCIDTadsuiRYuwcOFC5fOkpCTExMTg+PHjLD9OpqWyELBzM3QUREQt0jixeOqpp3QRR4vc3d1hYWHRqHciPz9f7dgOqVQKqVSqfG5vbw8AsLS0bFV3DpHREMkB/p8lIhPQqsRi2rRpWLduHRwdHTFt2rRm9920aZNWAruTRCJBVFQUdu3ahalTpyq379q1C5MnT9bJOYmMRl2FoSMgImqVViUWTk5OytsNjo6Oam896NrChQsxa9Ys9OnTB9HR0Vi9ejUyMzPx9NNPGyQeIr0pzwNcgwEL9loQkXFrVWKxdu1a5d/XrVunq1haNGPGDBQWFmLZsmXIzc1Fjx49sG3bNgQGBhosJiK9UNQDucmAX5ShIyEiapbGdSyGDx+OkpKSRtvLysp0Ot30lmeffRbp6emora3FqVOnMGTIEJ2fk8goXN1r6AiIiFqkcWKxf/9+1NXVNdpeU1PD8tpEunQ1HpDVtrwfEZEBtXpWSHJysvLv58+fV5mdIZfLERcXhw4dOmg3OiL6R205cGEr0OM+Q0dCRKRWqxOLXr16QSQSQSQSNXnLw8bGBl988YVWgyOiOySsBQKiAUdfQ0dCRNSkVicW165dgyAICA4OxokTJ+Dh4aFsk0gk8PT0hIWFhU6CJKK/1ZYD214Bxn8MOOh2bR4iorvR6sTi1swLhUKhs2CIqLE+ffogLysD3tIaJLzeGyjNAv54Fhi1HPDsZujwiIhU3NUiZEDDOIvMzMxGAzknTZrU5qCI6B95eXnIvlEAOEv+2VhZAGyZD/R9Egh/ABC3aaFiIiKt0TixuHr1KqZOnYqUlBSIRCLlgiS3imbJ5VzemUgv5PXAsVXAld1A37lAABfXIyLD0/hrzvPPP4+OHTvixo0bsLW1xblz53DgwAH06dMH+/fv10GIRNSsgsvA9leB/z0GnN0E1JQZOiIiasc07rE4evQo9u7dCw8PD4jFYojFYgwePBgrVqzAggULcPr0aV3ESUQtKboGHP6soRcjYADQZUzDn2IOqiYi/dE4sZDL5cpVQt3d3ZGTk4PQ0FAEBgbi4sWLWg+QiDQkrwOuHWh42LoCnUcDXccDzv6GjoyI2gGNE4sePXogOTkZwcHB6N+/P1auXAmJRILVq1cjODhYFzES0d2qKgLO/Nzw8OkJhI4FgocCVjaGjoyIzJTGicWbb76JyspKAMDy5csxYcIE3HPPPXBzc8PGjRu1HiBRe5aZmYmqqioAQFWdAplFNQhwtb67F8s90/A4/DkQHNNwq8S3l/aCJSICIBJuTetog6KiIri4uBhsOfXWSkxMRFRUFE6dOoXevXsbOhwitU6cOIG3334bf/31F26/REUiYEK4K94aF4i+QQ5tP5GzP9B9WsOtEktp21+PiNo9rUx+d3V1NfqkgshUbNq0CYMGDcL27dtxZ94vCMC2s0UYuDIJm04XtP1kJdcbBnz+/CBw/g+ABfCIqI00vhUyderUJpMIkUgEa2trhISE4OGHH0ZoaKhWAiRqT06cOIEZM2ZALpc3SipukSsAEQTMWJOKI6/20k7PRVURcPBj4NzvQPRzgF9U21+TiNoljXssnJycsHfvXiQmJioTjNOnT2Pv3r2QyWTYuHEjevbsicOHD2s9WCJzt3z5cgiCoDapuEUAIEDA8m0Z2g2g6Crw10Jg51tAbYV2X5uI2gWNEwtvb288/PDDuHr1Kn777Tds2rQJaWlpeOSRR9CpUyekpqZi9uzZ+Ne//qWLeInMVmZmJrZu3drq6rVyBfBnShEyi2q0H8y1Aw31MIiINKRxYvHNN9/ghRdegPi2tQnEYjHmz5+P1atXQyQS4bnnnsPZs2e1GiiRuduzZ0+LPRV3EgRg74US3QQUGK2b1yUis6ZxYiGTyXDhwoVG2y9cuKD8pmVtbc3BnEQaKi8vV0nYW0MsAspqdLA+T49pQNBg7b8uEZk9jQdvzpo1C3PmzMHrr7+Ovn37QiQS4cSJE3j33Xfx6KOPAgDi4+PRvXt3rQdLZM4cHByg0HBWhkIAHK21XLJbbAFEztLuaxJRu6FxYvHJJ5/Ay8sLK1euxI0bNwAAXl5eePHFF5XjKkaNGoUxY8ZoN1IiM3fvvfeqrBjcGiIRMLyrs3YD6TapoRQ4EdFdaFOBrLKyhlUUHR0dtRaQLrFAFhm7SZMmYdu2ba0awGkhBsb3cMUfz/bQzsktrICeDwFRjwMa3pIhIrrlrn56yGQy7N69Gz///LNyLEVOTg4qKjg9jagt3nrrLYhEohbHKIkAiCDCm+MC235SCwnQbSIw4weg7xwmFUTUJhrfCsnIyMCYMWOQmZmJ2tpajBw5Eg4ODli5ciVqamrw1Vdf6SJOonahb9++2LhxI2bMmAFBEJrsubAQNyQVv8zt1rbiWNZOQPcpQNgU3vogIq3R+KvJ888/jz59+qC4uBg2Nv+skDh16lTs2bNHq8ERtUfTpk3DkSNHMG7cuEY9FyJRw+2PI6/2wtRI97s7gZMfcM9CYOb/gD5PMKkgIq3SuMfi0KFDOHz4MCQSicr2wMBAZGdnay0wovasb9++2LJlCzIzM9GrVy8UFxfDxdYSSW/2vrvVTUViICAaCJsM+PdryFCIiHRA48RCoVA02T2blZUFBwctrFlAREoBAQGwtbVFcXExbCVizZMKO3eg64SGh72HboIkIrqNxrdCRo4ciU8//VT5XCQSoaKiArGxsRg3bpw2YyOiu+USCAx7A3hoI9DncSYVRKQ3d1XHYtiwYQgLC0NNTQ0efvhhXL58Ge7u7vj55591ESMRtZaDT8O4iZARnN1BRAahcWLh6+uLpKQk/Pzzz0hMTIRCocCcOXMwc+ZMlcGcRKRHtm5A5CMN00YtrAwdDRG1YxonFgBgY2ODJ554Ak888YS24yEiTVjZAL1mAhEPAJZSQ0dDRHR3icWlS5ewf/9+5OfnN1rbYPHixVoJjIha4NkNuDcWcPQxdCREREoaJxZr1qzBM888A3d3d3h7e6vMsxeJREwsiPTBvx8wajl7KYjI6GicWCxfvhzvvPOOcsExItItb29vQFYLb2lNwwbPbkwqiMhoaZxYFBcXY/r06bqIhYiakJCQAFzZDex5G5A6ACPfZlJBREZL4/lo06dPx86dO3URCxG1pN9c1qQgIqOmcY9FSEgI3nrrLRw7dgzh4eGwslKd2rZgwQKtBUdEt7HzAELHGzoKIqJmiQRBEDQ5oGPHjupfTCTC1atX2xyUriQmJiIqKgqnTp1C7969DR0OUetd2Q0UZzQsa05EZMQ07rG4du2aLuIgopZ0jDF0BERELTKpmr8HDhzAxIkT4evrC5FIhN9//93QIRHph8QBcOtk6CiIiFpkUolFZWUlevbsiX//+9+GDoVIv5z8uNQ5EZmEu6q8aShjx47F2LFjDR0Gkf5J7Q0dARFRq5hUjwVRuyXipUpEpsGkeiw0VVtbi9raWuXziooKA0ZD1AZWtoaOgIioVe7qa9DBgwfxyCOPIDo6GtnZ2QCA77//HocOHdJqcG21YsUKODk5KR8xMRxVTyZKbGHoCIiIWkXjxOK3337D6NGjYWNjg9OnTyt7BMrLy/Huu+9qPcC2WLRoEUpLS5WP+Ph4Q4dERERk1jROLJYvX46vvvoKa9asUam6OXDgQCQmJmo1uLaSSqVwdHRUPuztOQCOiIhIlzQeY3Hx4kUMGTKk0XZHR0eUlJRoIya1KioqcOXKFeXza9euISkpCa6urggICNDpuYmIiKhlGvdY+Pj4qPxyv+XQoUMIDg7WSlDqJCQkIDIyEpGRkQCAhQsXIjIyEosXL9bpeYmIiKh1NO6x+L//+z88//zz+PbbbyESiZCTk4OjR4/i5Zdf1vkv+KFDh0LDpU2IiIhIjzROLF599VWUlpZi2LBhqKmpwZAhQyCVSvHyyy/jueee00WMREREZCI0SizkcjkOHTqEl156CW+88QbOnz8PhUKBsLAwDowkIiIizRILCwsLjB49GqmpqXB1dUWfPn10FRcRERGZII0Hb4aHh+Pq1au6iIWIiIhMnMaJxTvvvIOXX34ZW7duRW5uLsrKylQeRERE1H5pPHhzzJgxAIBJkyZBdNsyzoIgQCQSQS6Xay86IiIiMikaJxb79u3TRRxERERkBjROLLiQFxEREalzV8uml5SU4JtvvkFqaipEIhHCwsLwxBNPwMnJSdvxERERkQnRePBmQkICOnXqhE8++QRFRUUoKCjAxx9/jE6dOhndImRERESkXxr3WLz44ouYNGkS1qxZA0vLhsNlMhmefPJJvPDCCzhw4IDWgyQiIiLToHFikZCQoJJUAIClpSVeffVVFswiIiJq5zS+FeLo6IjMzMxG269fvw4HBwetBEVERESmSePEYsaMGZgzZw42btyI69evIysrCxs2bMCTTz6Jhx56SBcxEhERkYnQ+FbIhx9+CJFIhEcffRQymQwAYGVlhWeeeQbvvfee1gMkIiIi0yESBEG4mwOrqqqQlpYGQRAQEhICW1tbbcemdYmJiYiKisKpU6fQu3dvQ4dDRERkdu6qjgUA2NraIjw8XJuxEBERkYlrdWLxxBNPtGq/b7/99q6DISIiItPW6sRi3bp1CAwMRGRkJO7y7gkRERGZuVYnFk8//TQ2bNiAq1ev4oknnsAjjzwCV1dXXcZGREREJqbV002//PJL5Obm4l//+hf+/PNP+Pv744EHHsCOHTvYg0FEREQANKxjIZVK8dBDD2HXrl04f/48unfvjmeffRaBgYGoqKjQVYxERERkIjQukHWLSCSCSCSCIAhQKBTajImIiIhMlEaJRW1tLX7++WeMHDkSoaGhSElJwb///W9kZmbC3t5eVzESERGRiWj14M1nn30WGzZsQEBAAB5//HFs2LABbm5uuoyNiIiITEyrK2+KxWIEBAQgMjISIpFI7X6bNm3SWnDaxsqbREREutXqHotHH3202YSCiIiISKMCWURERETNuetZIURERER3YmJBREREWsPEgoiIiLSGiQURERFpDRMLIiIi0homFkRERKQ1TCyIiIhIa5hYEBERkdYwsSAiIiKtYWJBREREWsPEgoiIiLSGiQURERFpjckkFitWrEDfvn3h4OAAT09PTJkyBRcvXjR0WERERHQbk0ks4uPjMW/ePBw7dgy7du2CTCbDqFGjUFlZaejQiIiI6G+tXjbd0OLi4lSer127Fp6enjh16hSGDBlioKiIiIjodiaTWNyptLQUAODq6qp2n9raWtTW1iqfV1RU6DwuY5Gbm4vc3FxDh0Fa4uPjAx8fH0OHQVrC69P88Bq9jWCCFAqFMHHiRGHw4MHN7hcbGysAUHnExMQIOTk5eorUMGpqaoSYmJhG750P033ExMQINTU1hv6vRVrA69M8H7xG/yESBEGAiZk3bx7++usvHDp0CH5+fmr3u7PHAgCkUimkUqmuQzSosrIyODk5IT4+Hvb29oYOh9qooqICMTExKC0thaOjo6HDoTbi9Wl+eI2qMrlbIfPnz8eWLVtw4MCBZpMKoH0kEc3p1asX/5ObgbKyMkOHQDrA69N88BpVZTKJhSAImD9/PjZv3oz9+/ejY8eOhg6JiIiI7mAyicW8efPw008/4Y8//oCDgwPy8vIAAE5OTrCxsTFwdERERASYUB2LVatWobS0FEOHDlWOvvXx8cHGjRsNHZrRkUqliI2Nbde3gcwJP0/zws/T/PAzVWWSgzeJiIjIOJlMjwUREREZPyYWREREpDVMLIiIiEhrmFgQERGR1jCxINIBkUjU7OOxxx6769cOCgrCp59+2uJ+q1evxtChQ+Ho6AiRSISSkpK7PieROTH09VlUVIT58+cjNDQUtra2CAgIwIIFC5RrYJk6k6ljQWRKbl9gauPGjVi8eDEuXryo3KaP2itVVVUYM2YMxowZg0WLFun8fESmwtDXZ05ODnJycvDhhx8iLCwMGRkZePrpp5GTk4Nff/1Vp+fWC4OuVELUDqxdu1ZwcnJS2bZlyxahd+/eglQqFTp27CgsWbJEqK+vV7bHxsYK/v7+gkQiEXx8fIT58+cLgiA0uXhVS/bt2ycAEIqLi7X5tojMgqGvz1t++eUXQSKRqJzHVLHHgkjPduzYgUceeQSff/457rnnHqSlpeGpp54CAMTGxuLXX3/FJ598gg0bNqB79+7Iy8vDmTNnAACbNm1Cz5498dRTT2Hu3LmGfBtEZslQ1+etBcwsLU3/17LpvwMiE/POO+/gtddew+zZswEAwcHBePvtt/Hqq68iNjYWmZmZ8Pb2xogRI2BlZYWAgAD069cPAODq6goLCws4ODjA29vbkG+DyCwZ4vosLCzE22+/jf/7v//TyXvSNw7eJNKzU6dOYdmyZbC3t1c+5s6di9zcXFRVVWH69Omorq5GcHAw5s6di82bN0Mmkxk6bKJ2Qd/XZ1lZGcaPH4+wsDDExsZq8Z0YDnssiPRMoVBg6dKlmDZtWqM2a2tr+Pv74+LFi9i1axd2796NZ599Fh988AHi4+NhZWVlgIiJ2g99Xp/l5eUYM2YM7O3tsXnzZrO5vplYEOlZ7969cfHiRYSEhKjdx8bGBpMmTcKkSZMwb948dO3aFSkpKejduzckEgnkcrkeIyZqP/R1fZaVlWH06NGQSqXYsmULrK2ttfk2DIqJBZGeLV68GBMmTIC/vz+mT58OsViM5ORkpKSkYPny5Vi3bh3kcjn69+8PW1tbfP/997CxsUFgYCCAhnnyBw4cwIMPPgipVAp3d/cmz5OXl4e8vDxcuXIFAJCSkgIHBwcEBATA1dVVb++XyJTo4/osLy/HqFGjUFVVhR9++AFlZWUoKysDAHh4eMDCwkKv71nrDD0thcjcNTWdLS4uThg4cKBgY2MjODo6Cv369RNWr14tCIIgbN68Wejfv7/g6Ogo2NnZCQMGDBB2796tPPbo0aNCRESEIJVKm53OFhsb22jqGwBh7dq1unibRCbJENfnrSngTT2uXbumq7eqN1w2nYiIiLSGs0KIiIhIa5hYEBERkdYwsSAiIiKtYWJBREREWsPEgsgI7N+/n0ubExkxXqOtx1khREagrq4ORUVF8PLygkgkMnQ4RHQHXqOtx8SCiIiItIa3Qoh0YOjQoZg/fz5eeOEFuLi4wMvLC6tXr0ZlZSUef/xxODg4oFOnTti+fTuAxt2s69atg7OzM3bs2IFu3brB3t4eY8aMQW5urso5XnjhBZXzTpkyBY899pjy+ZdffonOnTvD2toaXl5euP/++3X91olMAq9R3WFiQaQj3333Hdzd3XHixAnMnz8fzzzzDKZPn46BAwciMTERo0ePxqxZs1BVVdXk8VVVVfjwww/x/fff48CBA8jMzMTLL7/c6vMnJCRgwYIFWLZsGS5evIi4uDgMGTJEW2+PyOTxGtUNJhZEOtKzZ0+8+eab6Ny5MxYtWgQbGxu4u7tj7ty56Ny5MxYvXozCwkIkJyc3eXx9fT2++uor9OnTB71798Zzzz2HPXv2tPr8mZmZsLOzw4QJExAYGIjIyEgsWLBAW2+PyOTxGtUNJhZEOhIREaH8u4WFBdzc3BAeHq7c5uXlBQDIz89v8nhbW1t06tRJ+dzHx0ftvk0ZOXIkAgMDERwcjFmzZuHHH39U+82LqD3iNaobTCyIdMTKykrluUgkUtl2a2S5QqFo9fG3j7UWi8W4c+x1fX298u8ODg5ITEzEzz//DB8fHyxevBg9e/bkdDmiv/Ea1Q0mFkQmysPDQ2WgmFwux9mzZ1X2sbS0xIgRI7By5UokJycjPT0de/fu1XeoRO1Se71GLQ0dABHdneHDh2PhwoX466+/0KlTJ3zyyScq33S2bt2Kq1evYsiQIXBxccG2bdugUCgQGhpquKCJ2pH2eo0ysSAyUU888QTOnDmDRx99FJaWlnjxxRcxbNgwZbuzszM2bdqEJUuWoKamBp07d8bPP/+M7t27GzBqovajvV6jLJBFREREWsMxFkRERKQ1TCyIiIhIa5hYEBERkdYwsSAiIiKtYWJBZObuXDyJiIyLuV2jTCyINJCXl4f58+cjODgYUqkU/v7+mDhxokbrA7RGU6si6tLq1asxdOhQODo6mtUPOGp/zPEaLSoqwvz58xEaGgpbW1sEBARgwYIFKC0t1cv5NcU6FkStlJ6ejkGDBsHZ2RkrV65EREQE6uvrsWPHDsybNw8XLlzQazyCIEAul8PSsu2XcVVVFcaMGYMxY8Zg0aJFWoiOSP/M9RrNyclBTk4OPvzwQ4SFhSEjIwNPP/00cnJy8Ouvv2opWi0SiKhVxo4dK3To0EGoqKho1FZcXKz8e0ZGhjBp0iTBzs5OcHBwEKZPny7k5eUp22NjY4WePXsK69evFwIDAwVHR0dhxowZQllZmSAIgjB79mwBgMrj2rVrwr59+wQAQlxcnBAVFSVYWVkJe/fuFWpqaoT58+cLHh4eglQqFQYNGiScOHFCeb5bx90eozqa7EtkbNrDNXrLL7/8IkgkEqG+vl7zfygd460QolYoKipCXFwc5s2bBzs7u0btzs7OABq+oUyZMgVFRUWIj4/Hrl27kJaWhhkzZqjsn5aWht9//x1bt27F1q1bER8fj/feew8A8NlnnyE6Ohpz585Fbm4ucnNz4e/vrzz21VdfxYoVK5CamoqIiAi8+uqr+O233/Ddd98hMTERISEhGD16NIqKinT3D0JkZNrbNVpaWgpHR0et9FhqnaEzGyJTcPz4cQGAsGnTpmb327lzp2BhYSFkZmYqt507d04AoPyGEhsbK9ja2iq//QiCILzyyitC//79lc9jYmKE559/XuW1b32r+f3335XbKioqBCsrK+HHH39UbqurqxN8fX2FlStXqhzHHgsyZ+3lGhUEQSgoKBACAgKEN954o1X76xt7LIhaQfi78v2tZZTVSU1Nhb+/v8q3l7CwMDg7OyM1NVW5LSgoCA4ODsrnPj4+yM/Pb1Usffr0Uf49LS0N9fX1GDRokHKblZUV+vXrp3I+InPXXq7RsrIyjB8/HmFhYYiNjdX4eH1gYkHUCp07d4ZIJGrxB4EgCE3+YLtzu5WVlUq7SCSCQqFoVSy3d/Oq+2GqLg4ic9UertHy8nKMGTMG9vb22Lx5c6MYjQUTC6JWcHV1xejRo/Gf//wHlZWVjdpvTc8MCwtDZmYmrl+/rmw7f/48SktL0a1bt1afTyKRQC6Xt7hfSEgIJBIJDh06pNxWX1+PhIQEjc5HZOrM/RotKyvDqFGjIJFIsGXLFlhbW7f6WH1jYkHUSl9++SXkcjn69euH3377DZcvX0Zqaio+//xzREdHAwBGjBiBiIgIzJw5E4mJiThx4gQeffRRxMTEqHSPtiQoKAjHjx9Heno6CgoK1H5TsrOzwzPPPINXXnkFcXFxOH/+PObOnYuqqirMmTOn1efLy8tDUlISrly5AgBISUlBUlISB4CSSTHXa7S8vByjRo1CZWUlvvnmG5SVlSEvLw95eXmtSm70zlCDO4hMUU5OjjBv3jwhMDBQkEgkQocOHYRJkyYJ+/btU+7T2qlst/vkk0+EwMBA5fOLFy8KAwYMEGxsbBpNZbtzgFd1dbUwf/58wd3d/a6nssXGxjaaPgdAWLt27V38KxEZjjleo7fam3pcu3btLv+ldEckCH/fACIiIiJqI94KISIiIq1hYkFERERaw8SCiIiItIaJBREREWkNEwsiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJBREREWnN/wMXWXlkdllzwAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_2group.mean_diff.plot(contrast_ylim=(2, -2),\n", + " contrast_label=\"More negative is better!\");" + ] + }, + { + "cell_type": "markdown", + "id": "5c0f96f8", + "metadata": {}, + "source": [ + "The contrast axes share the same y-limits as that of the delta - delta plot\n", + "and thus the y axis of the delta - delta plot changes as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d588b8d3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "\n", + "# Create samples\n", + "N = 20\n", + "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n", + "y[N:2*N] = y[N:2*N]+1\n", + "y[2*N:3*N] = y[2*N:3*N]-0.5\n", + "\n", + "# Add a `Treatment` column\n", + "t1 = np.repeat('Placebo', N*2).tolist()\n", + "t2 = np.repeat('Drug', N*2).tolist()\n", + "treatment = t1 + t2 \n", + "\n", + "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n", + "rep = []\n", + "for i in range(N*2):\n", + " rep.append('Rep1')\n", + " rep.append('Rep2')\n", + "\n", + "# Add a `Genotype` column as the second variable\n", + "wt = np.repeat('W', N).tolist()\n", + "mt = np.repeat('M', N).tolist()\n", + "wt2 = np.repeat('W', N).tolist()\n", + "mt2 = np.repeat('M', N).tolist()\n", + "\n", + "\n", + "genotype = wt + mt + wt2 + mt2\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id = list(range(0, N*2))\n", + "id_col = id + id \n", + "\n", + "\n", + "# Combine all columns into a DataFrame.\n", + "df_delta2 = pd.DataFrame({'ID' : id_col,\n", + " 'Rep' : rep,\n", + " 'Genotype' : genotype, \n", + " 'Treatment': treatment,\n", + " 'Y' : y\n", + " })\n", + "\n", + "paired_delta2 = dabest.load(data = df_delta2, \n", + " paired = \"baseline\", id_col=\"ID\",\n", + " x = [\"Treatment\", \"Rep\"], y = \"Y\", \n", + " delta2 = True, experiment = \"Genotype\")\n", + "paired_delta2.mean_diff.plot(contrast_ylim=(3, -3),\n", + " contrast_label=\"More negative is better!\");" + ] + }, + { + "cell_type": "markdown", + "id": "7682de82", + "metadata": {}, + "source": [ + "You can also change the y-limits and y-label for the delta - delta plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "856301bb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "paired_delta2.mean_diff.plot(delta2_ylim=(3, -3),\n", + " delta2_label=\"More negative is better!\");" + ] + }, + { + "cell_type": "markdown", + "id": "a60c4367", + "metadata": {}, + "source": [ + "You can add minor ticks and also change the tick frequency by accessing\n", + "the axes directly.\n", + "\n", + "Each estimation plot produced by ``dabest`` has 2 axes. The first one\n", + "contains the rawdata swarmplot; the second one contains the bootstrap\n", + "effect size differences.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c2f3504", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.ticker as Ticker\n", + "\n", + "f = two_groups_unpaired.mean_diff.plot()\n", + "\n", + "rawswarm_axes = f.axes[0]\n", + "contrast_axes = f.axes[1]\n", + "\n", + "rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1))\n", + "rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5))\n", + "\n", + "contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\n", + "contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc0f29ec", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = multi_2group.mean_diff.plot(swarm_ylim=(0,6),\n", + " contrast_ylim=(-3, 1))\n", + "\n", + "rawswarm_axes = f.axes[0]\n", + "contrast_axes = f.axes[1]\n", + "\n", + "rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(2))\n", + "rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(1))\n", + "\n", + "contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\n", + "contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))" + ] + }, + { + "cell_type": "markdown", + "id": "ec7f5271", + "metadata": {}, + "source": [ + "For mini-meta plots, you can hide the weighted avergae plot by setting \n", + "``show_mini_meta=False`` in the ``plot()`` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "337fa39d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "# pop_size = 10000 # Size of each population.\n", + "Ns = 20 # The number of samples taken from each population\n", + "\n", + "# Create samples\n", + "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n", + "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n", + "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n", + "\n", + "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n", + "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n", + "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n", + "\n", + "\n", + "# Add a `gender` column for coloring the data.\n", + "females = np.repeat('Female', Ns/2).tolist()\n", + "males = np.repeat('Male', Ns/2).tolist()\n", + "gender = females + males\n", + "\n", + "# Add an `id` column for paired data plotting.\n", + "id_col = pd.Series(range(1, Ns+1))\n", + "\n", + "# Combine samples and gender into a DataFrame.\n", + "df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " 'Control 2' : c2, 'Test 2' : t2,\n", + " 'Control 3' : c3, 'Test 3' : t3,\n", + " 'Gender' : gender, 'ID' : id_col\n", + " })\n", + "mini_meta_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n", + "mini_meta_paired.mean_diff.plot(show_mini_meta=False);" + ] + }, + { + "cell_type": "markdown", + "id": "659d880a", + "metadata": {}, + "source": [ + "Similarly, you can also hide the delta-delta plot by setting \n", + "``show_delta2=False`` in the ``plot()`` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2984546", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "paired_delta2.mean_diff.plot(show_delta2=False);" + ] + }, + { + "cell_type": "markdown", + "id": "aa66a227", + "metadata": {}, + "source": [ + "## Creating estimation plots in existing axes" + ] + }, + { + "cell_type": "markdown", + "id": "ba3ebef2", + "metadata": {}, + "source": [ + "*Implemented in v0.2.6 by Adam Nekimken*.\n", + "\n", + "``dabest.plot`` has an ``ax`` keyword that accepts any Matplotlib\n", + "``Axes``. The entire estimation plot will be created in the specified\n", + "``Axes``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a2aa538", + "metadata": {}, + "outputs": [], + "source": [ + "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", + " paired=\"baseline\", id_col=\"ID\")\n", + "multi_2group_paired = dabest.load(df,\n", + " idx=((\"Control 1\", \"Test 1\"),\n", + " (\"Control 2\", \"Test 2\")),\n", + " paired=\"baseline\", id_col=\"ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9624ce3b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "f, axx = plt.subplots(nrows=2, ncols=2,\n", + " figsize=(15, 15),\n", + " gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing.\n", + " )\n", + "\n", + "two_groups_unpaired.mean_diff.plot(ax=axx.flat[0]);\n", + "\n", + "two_groups_paired_baseline.mean_diff.plot(ax=axx.flat[1]);\n", + "\n", + "multi_2group.mean_diff.plot(ax=axx.flat[2]);\n", + "\n", + "multi_2group_paired.mean_diff.plot(ax=axx.flat[3]);" + ] + }, + { + "cell_type": "markdown", + "id": "c793b67c", + "metadata": {}, + "source": [ + "In this case, to access the individual rawdata axes, use\n", + "``name_of_axes`` to manipulate the rawdata swarmplot axes, and\n", + "``name_of_axes.contrast_axes`` to gain access to the effect size axes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad858bba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(638.7222222222223, 0.5, 'New y-axis label for effect size')" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topleft_axes = axx.flat[0]\n", + "topleft_axes.set_ylabel(\"New y-axis label for rawdata\")\n", + "topleft_axes.contrast_axes.set_ylabel(\"New y-axis label for effect size\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4872a5d1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tutorials/index.qmd b/nbs/tutorials/index.qmd new file mode 100644 index 00000000..46d74058 --- /dev/null +++ b/nbs/tutorials/index.qmd @@ -0,0 +1,11 @@ +--- +order: 1 +title: Tutorials +listing: + fields: [title, description] + type: table + sort-ui: false + filter-ui: false +--- + +Click through to any of these tutorials to get started with dabest's features. \ No newline at end of file diff --git a/settings.ini b/settings.ini new file mode 100644 index 00000000..a887eb8b --- /dev/null +++ b/settings.ini @@ -0,0 +1,46 @@ +[DEFAULT] +### Python library ### +repo = DABEST-python +lib_name = dabest +version = 2023.2.14 +min_python = 3.7 +license = apache2 + +### nbdev ### +doc_path = _docs +lib_path = dabest +nbs_path = nbs +recursive = True +tst_flags = notest +put_version_in_init = True +readme_nb = read_me.ipynb +jupyter_hooks = True + +### Docs ### +branch = master +custom_sidebar = True +doc_host = https://%(user)s.github.io +doc_baseurl = /%(repo)s +git_url = https://github.com/%(user)s/%(repo)s +title = %(lib_name)s + +### PyPI ### +audience = Developers +author = Joses W. Ho +author_email = joseshowh@gmail.com +maintainer = Adam Claridge-Chang +maintainer_email = estimationstats@gmail.com +copyright = 2023 onwards, %(author)s +description = Data Analysis and Visualization using Bootstrap-Coupled Estimation. +keywords = nbdev jupyter notebook python +language = English +status = 3 +user = ZHANGROU-99 + +requirements = fastcore pandas~=1.5.0 numpy~=1.22.3 matplotlib~=3.5.1 seaborn~=0.11.2 scipy~=1.9.3 datetime statsmodels lqrt +dev_requirements = pytest~=7.1.3 pytest-mpl~=0.16.1 + +### Optional ### +# requirements = fastcore pandas +# dev_requirements = +# console_scripts = \ No newline at end of file diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 79bc6784..00000000 --- a/setup.cfg +++ /dev/null @@ -1,5 +0,0 @@ -[bdist_wheel] -# This flag says that the code is written to work on both Python 2 and Python -# 3. If at all possible, it is good practice to do this. If you cannot, you -# will need to generate wheels for each Python version that you support. -universal=1 diff --git a/setup.py b/setup.py index a7cffe84..34abbe8b 100644 --- a/setup.py +++ b/setup.py @@ -1,67 +1,57 @@ -from setuptools import setup, find_packages -import os -# Taken from setup.py in seaborn. -# temporarily redirect config directory to prevent matplotlib importing -# testing that for writeable directory which results in sandbox error in -# certain easy_install versions -os.environ["MPLCONFIGDIR"]="." +from pkg_resources import parse_version +from configparser import ConfigParser +import setuptools, shlex +assert parse_version(setuptools.__version__)>=parse_version('36.2') -DESCRIPTION = 'Data Analysis and Visualization using Bootstrap-Coupled Estimation.' -LONG_DESCRIPTION = """\ -Estimation statistics is a simple framework -that—while avoiding the pitfalls of significance testing—uses familiar statistical -concepts: means, mean differences, and error bars. More importantly, it focuses on -the effect size of one's experiment/intervention, as opposed to -significance testing. +# note: all settings are in settings.ini; edit there, not here +config = ConfigParser(delimiters=['=']) +config.read('settings.ini') +cfg = config['DEFAULT'] -An estimation plot has two key features. Firstly, it presents all -datapoints as a swarmplot, which orders each point to display the -underlying distribution. Secondly, an estimation plot presents the -effect size as a bootstrap 95% confidence interval on a separate but -aligned axes. +cfg_keys = 'version description keywords author author_email'.split() +expected = cfg_keys + "lib_name user branch license status min_python audience language".split() +for o in expected: assert o in cfg, "missing expected setting: {}".format(o) +setup_cfg = {o:cfg[o] for o in cfg_keys} -Please cite this work as: -Moving beyond P values: Everyday data analysis with estimation plots -Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang -https://doi.org/10.1101/377978 -""" +licenses = { + 'apache2': ('Apache Software License 2.0','OSI Approved :: Apache Software License'), + 'mit': ('MIT License', 'OSI Approved :: MIT License'), + 'gpl2': ('GNU General Public License v2', 'OSI Approved :: GNU General Public License v2 (GPLv2)'), + 'gpl3': ('GNU General Public License v3', 'OSI Approved :: GNU General Public License v3 (GPLv3)'), + 'bsd3': ('BSD License', 'OSI Approved :: BSD License'), +} +statuses = [ '1 - Planning', '2 - Pre-Alpha', '3 - Alpha', + '4 - Beta', '5 - Production/Stable', '6 - Mature', '7 - Inactive' ] +py_versions = '3.6 3.7 3.8 3.9 3.10'.split() +requirements = shlex.split(cfg.get('requirements', '')) +if cfg.get('pip_requirements'): requirements += shlex.split(cfg.get('pip_requirements', '')) +min_python = cfg['min_python'] +lic = licenses.get(cfg['license'].lower(), (cfg['license'], None)) +dev_requirements = (cfg.get('dev_requirements') or '').split() + +setuptools.setup( + name = cfg['lib_name'], + license = lic[0], + classifiers = [ + 'Development Status :: ' + statuses[int(cfg['status'])], + 'Intended Audience :: ' + cfg['audience'].title(), + 'Natural Language :: ' + cfg['language'].title(), + ] + ['Programming Language :: Python :: '+o for o in py_versions[py_versions.index(min_python):]] + (['License :: ' + lic[1] ] if lic[1] else []), + url = cfg['git_url'], + packages = setuptools.find_packages(), + include_package_data = True, + install_requires = requirements, + extras_require={ 'dev': dev_requirements }, + dependency_links = cfg.get('dep_links','').split(), + python_requires = '>=' + cfg['min_python'], + long_description = open('README.md').read(), + long_description_content_type = 'text/markdown', + zip_safe = False, + entry_points = { + 'console_scripts': cfg.get('console_scripts','').split(), + 'nbdev': [f'{cfg.get("lib_path")}={cfg.get("lib_path")}._modidx:d'] + }, + **setup_cfg) -if __name__ == "__main__": - setup( - name='dabest', - author='Joses W. Ho', - author_email='joseshowh@gmail.com', - maintainer='Joses W. Ho', - maintainer_email='joseshowh@gmail.com', - version='0.3.1', - description=DESCRIPTION, - long_description=LONG_DESCRIPTION, - packages=find_packages(), - install_requires=[ - 'numpy~=1.19', - 'scipy~=1.5', - 'pandas~=1.1', - 'matplotlib~=3.3', - 'seaborn~=0.11', - 'lqrt~=0.3' - ], - extras_require={'dev': ['pytest~=6.1', 'pytest-mpl~=0.11']}, - python_requires='~=3.6', - classifiers=[ - "Development Status :: 5 - Production/Stable", - "Intended Audience :: Science/Research", - "Intended Audience :: Education", - "License :: OSI Approved :: BSD License", - "Programming Language :: Python :: 3", - "Topic :: Scientific/Engineering :: Visualization", - "Operating System :: Microsoft :: Windows", - "Operating System :: POSIX :: Linux", - "Operating System :: Unix", - "Operating System :: MacOS", - ], - url='https://acclab.github.io/DABEST-python-docs', - download_url='https://www.github.com/ACCLAB/DABEST-python', - license='BSD 3-clause Clear License' - ) diff --git a/showpiece.png b/showpiece.png new file mode 100644 index 00000000..6e46cd50 Binary files /dev/null and b/showpiece.png differ