├── .gitignore ├── LICENSE ├── README.md ├── data ├── 4_Controls Hare Live trap data Kluane.xlsx ├── AirPassengers.csv ├── SP500.csv ├── TNNASHVI.txt ├── ads.csv ├── cancer.csv ├── cdystonia.csv ├── cty.dat ├── currency.csv ├── marathon_results_2015.csv ├── microbiome.csv ├── nyc_taxi.csv ├── phe_iq.csv ├── pku_data.csv ├── radon.csv ├── salmon.txt ├── srrs2.dat ├── taxi_rides_2016.csv ├── vlbw.csv ├── walker.txt └── weather_central_park_2016.csv ├── environment.yml └── notebooks ├── Section0-Pre_Work.ipynb ├── Section1-Homework.ipynb ├── Section1_1-Basic_Bayes.ipynb ├── Section1_2-Hierarchical_Models.ipynb ├── Section2_1-MCMC.ipynb ├── Section3-Homework.ipynb ├── Section3_1-Gradient-Based-MCMC.ipynb ├── Section3_2-Model-Checking.ipynb ├── Section3_3-Model_Comparison.ipynb ├── Section4_1-Workflow.ipynb ├── Section5-Homework.ipynb ├── Section5_1-Gaussian_Processes.ipynb ├── Section5_2-Dirichlet_Processes.ipynb ├── Section6_1-Bayesian_Time_Series.ipynb ├── Section6_1-Homework.ipynb ├── Section6_1-Solution.ipynb ├── Section6_2-Survival_Models.ipynb ├── electricity_demand_data.py └── images ├── 123.png ├── HMC_samples.png ├── Sunspots_1302_Sep_2011_by_NASA.jpg ├── animated_sample.mp4 ├── bayes.png ├── bayes_opt_functions.png ├── binary_doubling.png ├── chinese_restaurant.jpg ├── diverging_hmc.png ├── fisher.png ├── funnel_leapfrog.png ├── hamiltonian_dynamics.png ├── hmc_examples.mp4 ├── how_radon_enters.jpg ├── integrator_pdf.png ├── mode_volume.png ├── multi_animated_sample.mp4 ├── nonlinear_salmon.png ├── partially_pooled_model.png ├── pku_newborn.jpg ├── polynomial.png ├── pooled_model.png ├── prob_model.png ├── skate_park.jpg ├── skate_park.png ├── spawn.jpg ├── state_space.png ├── straight_line.png ├── time_series_cv.png ├── unpooled_model.png └── uturn.png /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | .DS_Store 107 | .vscode/ 108 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Chris Fonnesbeck 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Bayesian Computing Course 2 | 3 | Material for course on Bayesian Computation 4 | 5 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fonnesbeck/Bayes_Computing_Course/master) 6 | 7 | ## Setup 8 | 9 | This tutorial assumes that you have [Anaconda](https://www.anaconda.com/products/individual#download-section) (Python 3.7) setup and installed on your system. If you do not, please download and install Anaconda on your system before proceeding with the setup. 10 | 11 | The next step is to clone or download the tutorial materials in this repository. If you are familiar with Git, run the clone command: 12 | 13 | git clone https://github.com/fonnesbeck/Bayes_Computing_Course.git 14 | 15 | otherwise you can [download a zip file](https://github.com/fonnesbeck/Bayes_Computing_Course/archive/master.zip) of its contents, and unzip it on your computer. 16 | *** 17 | The repository for this tutorial contains a file called `environment.yml` that includes a list of all the packages used for the tutorial. If you run: 18 | 19 | conda env create 20 | 21 | from the main tutorial directory, it will create the environment for you and install all of the packages listed. This environment can be enabled using: 22 | 23 | conda activate bayes_course 24 | 25 | Then, you can start **JupyterLab** to access the materials: 26 | 27 | jupyter lab 28 | 29 | The binder link above should also provide a working environment. 30 | 31 | ## Pre-course Work 32 | 33 | In advance of the course, we would like attendees to complete a short homework notebook that will ensure everyone has the requisite baseline knowledge. You can find this Jupyter notebook in the `/notebooks` subdirectory (under `Section0-Pre_Work.ipynb`). There is no need to hand this in to anyone, but please reach out if you have difficulty with any of the problems (or with setting up your computing environment) by creating an [issue](https://github.com/fonnesbeck/Bayes_Computing_Course/issues) in this repository, or by emailing. 34 | 35 | ## Course Outline 36 | 37 | The course comprises six 2-hour modules of videoconference lectures, along with short associated hands-on projects to reinforce materials covered during lectures. The first four sections cover core materials related to Bayesian computation, while the final two modules are elective material chosen by your group, which extend the concepts covered by the core sections to specific topics: time series modeling and non-parametric Bayesian models. 38 | 39 | ### Monday, July 13 40 | 41 | **Hierarchcial Models** (Chris) 2:00pm - 4:00pm 42 | - Motivation and case studies 43 | - Partial pooling 44 | - Building hierarchical models 45 | - Parameterizations 46 | - Model checking 47 | 48 | ### Wednesday, July 15 49 | 50 | **Markov chain Monte Carlo** (Thomas) 2:00pm - 4:00pm 51 | - Probability density functions, inverse CDF sampling 52 | - Rejection sampling 53 | - MCMC basics 54 | - Metropolis-Hastings samplers 55 | - Gibbs samplers 56 | 57 | ### Friday, July 17 58 | 59 | **Gradient-based MCMC** (Chris) 2:00pm - 4:00pm 60 | - Problems with first-generation MCMC methods 61 | - Using gradient information to improve MCMC 62 | - Hamiltonian Monte Carlo 63 | - NUTS 64 | - Diagnostics 65 | 66 | 67 | ### Monday, July 20 68 | 69 | **The Bayesian Workflow** (Thomas) 2:00pm - 4:00pm 70 | - Prior predictive checks 71 | - Iterating models 72 | - Posterior predictive checks 73 | - Using the model 74 | 75 | ### Wednesday, July 22 76 | 77 | **Bayesian Non-parametric Models** (Chris) 2:00pm - 4:00pm 78 | - Kernel-based models 79 | - Modeling with Gaussian distributions 80 | - Gaussian processes 81 | - Covariance functions 82 | - Bayesian optimization 83 | 84 | ### Friday, July 24 85 | 86 | **Bayesian Time Series Models** (Thomas) 2:00pm - 4:00pm 87 | - Modeling repeated measurements 88 | - Structural time series models 89 | - Hierarchical time series models 90 | - Censored data and survival models 91 | - Model checking 92 | -------------------------------------------------------------------------------- /data/4_Controls Hare Live trap data Kluane.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fonnesbeck/Bayes_Computing_Course/431bf0a0e2fc9970cfca8b8192318186fdace850/data/4_Controls Hare Live trap data Kluane.xlsx -------------------------------------------------------------------------------- /data/AirPassengers.csv: -------------------------------------------------------------------------------- 1 | Month,#Passengers 2 | 1949-01,112 3 | 1949-02,118 4 | 1949-03,132 5 | 1949-04,129 6 | 1949-05,121 7 | 1949-06,135 8 | 1949-07,148 9 | 1949-08,148 10 | 1949-09,136 11 | 1949-10,119 12 | 1949-11,104 13 | 1949-12,118 14 | 1950-01,115 15 | 1950-02,126 16 | 1950-03,141 17 | 1950-04,135 18 | 1950-05,125 19 | 1950-06,149 20 | 1950-07,170 21 | 1950-08,170 22 | 1950-09,158 23 | 1950-10,133 24 | 1950-11,114 25 | 1950-12,140 26 | 1951-01,145 27 | 1951-02,150 28 | 1951-03,178 29 | 1951-04,163 30 | 1951-05,172 31 | 1951-06,178 32 | 1951-07,199 33 | 1951-08,199 34 | 1951-09,184 35 | 1951-10,162 36 | 1951-11,146 37 | 1951-12,166 38 | 1952-01,171 39 | 1952-02,180 40 | 1952-03,193 41 | 1952-04,181 42 | 1952-05,183 43 | 1952-06,218 44 | 1952-07,230 45 | 1952-08,242 46 | 1952-09,209 47 | 1952-10,191 48 | 1952-11,172 49 | 1952-12,194 50 | 1953-01,196 51 | 1953-02,196 52 | 1953-03,236 53 | 1953-04,235 54 | 1953-05,229 55 | 1953-06,243 56 | 1953-07,264 57 | 1953-08,272 58 | 1953-09,237 59 | 1953-10,211 60 | 1953-11,180 61 | 1953-12,201 62 | 1954-01,204 63 | 1954-02,188 64 | 1954-03,235 65 | 1954-04,227 66 | 1954-05,234 67 | 1954-06,264 68 | 1954-07,302 69 | 1954-08,293 70 | 1954-09,259 71 | 1954-10,229 72 | 1954-11,203 73 | 1954-12,229 74 | 1955-01,242 75 | 1955-02,233 76 | 1955-03,267 77 | 1955-04,269 78 | 1955-05,270 79 | 1955-06,315 80 | 1955-07,364 81 | 1955-08,347 82 | 1955-09,312 83 | 1955-10,274 84 | 1955-11,237 85 | 1955-12,278 86 | 1956-01,284 87 | 1956-02,277 88 | 1956-03,317 89 | 1956-04,313 90 | 1956-05,318 91 | 1956-06,374 92 | 1956-07,413 93 | 1956-08,405 94 | 1956-09,355 95 | 1956-10,306 96 | 1956-11,271 97 | 1956-12,306 98 | 1957-01,315 99 | 1957-02,301 100 | 1957-03,356 101 | 1957-04,348 102 | 1957-05,355 103 | 1957-06,422 104 | 1957-07,465 105 | 1957-08,467 106 | 1957-09,404 107 | 1957-10,347 108 | 1957-11,305 109 | 1957-12,336 110 | 1958-01,340 111 | 1958-02,318 112 | 1958-03,362 113 | 1958-04,348 114 | 1958-05,363 115 | 1958-06,435 116 | 1958-07,491 117 | 1958-08,505 118 | 1958-09,404 119 | 1958-10,359 120 | 1958-11,310 121 | 1958-12,337 122 | 1959-01,360 123 | 1959-02,342 124 | 1959-03,406 125 | 1959-04,396 126 | 1959-05,420 127 | 1959-06,472 128 | 1959-07,548 129 | 1959-08,559 130 | 1959-09,463 131 | 1959-10,407 132 | 1959-11,362 133 | 1959-12,405 134 | 1960-01,417 135 | 1960-02,391 136 | 1960-03,419 137 | 1960-04,461 138 | 1960-05,472 139 | 1960-06,535 140 | 1960-07,622 141 | 1960-08,606 142 | 1960-09,508 143 | 1960-10,461 144 | 1960-11,390 145 | 1960-12,432 146 | -------------------------------------------------------------------------------- /data/SP500.csv: 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2017-09-21T01:00:00,72150 2017-09-21T02:00:00,80195 2017-09-21T03:00:00,94945 2017-09-21T04:00:00,121910 2017-09-21T05:00:00,113950 2017-09-21T06:00:00,106495 2017-09-21T07:00:00,97290 2017-09-21T08:00:00,98860 2017-09-21T09:00:00,105635 2017-09-21T10:00:00,114380 2017-09-21T11:00:00,132335 2017-09-21T12:00:00,146630 2017-09-21T13:00:00,141995 2017-09-21T14:00:00,142815 2017-09-21T15:00:00,146020 2017-09-21T16:00:00,152120 2017-09-21T17:00:00,151790 2017-09-21T18:00:00,155665 2017-09-21T19:00:00,155890 2017-09-21T20:00:00,123395 2017-09-21T21:00:00,103080 2017-09-21T22:00:00,95155 2017-09-21T23:00:00,80285 -------------------------------------------------------------------------------- /data/cancer.csv: -------------------------------------------------------------------------------- 1 | y,n 2 | 0,1083 3 | 0,855 4 | 2,3461 5 | 0,657 6 | 1,1208 7 | 1,1025 8 | 0,527 9 | 2,1668 10 | 1,583 11 | 3,582 12 | 0,917 13 | 1,857 14 | 1,680 15 | 1,917 16 | 54,53637 17 | 0,874 18 | 0,395 19 | 1,581 20 | 3,588 21 | 0,383 22 | -------------------------------------------------------------------------------- /data/cdystonia.csv: -------------------------------------------------------------------------------- 1 | patient,obs,week,site,id,treat,age,sex,twstrs 2 | 1,1,0,1,1,5000U,65,F,32 3 | 1,2,2,1,1,5000U,65,F,30 4 | 1,3,4,1,1,5000U,65,F,24 5 | 1,4,8,1,1,5000U,65,F,37 6 | 1,5,12,1,1,5000U,65,F,39 7 | 1,6,16,1,1,5000U,65,F,36 8 | 2,1,0,1,2,10000U,70,F,60 9 | 2,2,2,1,2,10000U,70,F,26 10 | 2,3,4,1,2,10000U,70,F,27 11 | 2,4,8,1,2,10000U,70,F,41 12 | 2,5,12,1,2,10000U,70,F,65 13 | 2,6,16,1,2,10000U,70,F,67 14 | 3,1,0,1,3,5000U,64,F,44 15 | 3,2,2,1,3,5000U,64,F,20 16 | 3,3,4,1,3,5000U,64,F,23 17 | 3,4,8,1,3,5000U,64,F,26 18 | 3,5,12,1,3,5000U,64,F,35 19 | 3,6,16,1,3,5000U,64,F,35 20 | 4,1,0,1,4,Placebo,59,F,53 21 | 4,2,2,1,4,Placebo,59,F,61 22 | 4,3,4,1,4,Placebo,59,F,64 23 | 4,4,8,1,4,Placebo,59,F,62 24 | 5,1,0,1,5,10000U,76,F,53 25 | 5,2,2,1,5,10000U,76,F,35 26 | 5,3,4,1,5,10000U,76,F,48 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273 | 46,6,16,4,8,10000U,52,F,33 274 | 47,1,0,5,1,10000U,51,F,44 275 | 47,2,2,5,1,10000U,51,F,34 276 | 47,3,4,5,1,10000U,51,F,32 277 | 47,4,8,5,1,10000U,51,F,35 278 | 47,5,12,5,1,10000U,51,F,54 279 | 47,6,16,5,1,10000U,51,F,53 280 | 48,1,0,5,2,Placebo,56,F,60 281 | 48,2,2,5,2,Placebo,56,F,57 282 | 48,3,4,5,2,Placebo,56,F,53 283 | 48,4,8,5,2,Placebo,56,F,52 284 | 48,5,12,5,2,Placebo,56,F,53 285 | 48,6,16,5,2,Placebo,56,F,58 286 | 49,1,0,5,3,5000U,65,F,60 287 | 49,2,2,5,3,5000U,65,F,53 288 | 49,3,4,5,3,5000U,65,F,55 289 | 49,4,8,5,3,5000U,65,F,62 290 | 49,5,12,5,3,5000U,65,F,67 291 | 50,1,0,5,4,10000U,35,F,50 292 | 50,2,2,5,4,10000U,35,F,50 293 | 50,4,8,5,4,10000U,35,F,46 294 | 50,5,12,5,4,10000U,35,F,50 295 | 50,6,16,5,4,10000U,35,F,57 296 | 51,1,0,5,5,5000U,43,M,38 297 | 51,2,2,5,5,5000U,43,M,27 298 | 51,3,4,5,5,5000U,43,M,16 299 | 51,4,8,5,5,5000U,43,M,19 300 | 51,5,12,5,5,5000U,43,M,23 301 | 51,6,16,5,5,5000U,43,M,26 302 | 52,1,0,5,6,Placebo,61,M,44 303 | 52,3,4,5,6,Placebo,61,M,46 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-------------------------------------------------------------------------------- 1 | Taxon,Patient,Group,Tissue,Stool 2 | Firmicutes,1,0,136,4182 3 | Firmicutes,2,1,1174,703 4 | Firmicutes,3,0,408,3946 5 | Firmicutes,4,1,831,8605 6 | Firmicutes,5,0,693,50 7 | Firmicutes,6,1,718,717 8 | Firmicutes,7,0,173,33 9 | Firmicutes,8,1,228,80 10 | Firmicutes,9,0,162,3196 11 | Firmicutes,10,1,372,32 12 | Firmicutes,11,0,4255,4361 13 | Firmicutes,12,1,107,1667 14 | Firmicutes,13,0,96,223 15 | Firmicutes,14,1,281,2377 16 | Proteobacteria,1,0,2469,1821 17 | Proteobacteria,2,1,839,661 18 | Proteobacteria,3,0,4414,18 19 | Proteobacteria,4,1,12044,83 20 | Proteobacteria,5,0,2310,12 21 | Proteobacteria,6,1,3053,547 22 | Proteobacteria,7,0,395,2174 23 | Proteobacteria,8,1,2651,767 24 | Proteobacteria,9,0,1195,76 25 | Proteobacteria,10,1,6857,795 26 | Proteobacteria,11,0,483,666 27 | Proteobacteria,12,1,2950,3994 28 | Proteobacteria,13,0,1541,816 29 | Proteobacteria,14,1,1307,53 30 | Actinobacteria,1,0,1590,4 31 | Actinobacteria,2,1,25,2 32 | Actinobacteria,3,0,259,300 33 | Actinobacteria,4,1,568,7 34 | Actinobacteria,5,0,1102,9 35 | Actinobacteria,6,1,678,377 36 | Actinobacteria,7,0,260,58 37 | Actinobacteria,8,1,424,233 38 | Actinobacteria,9,0,548,21 39 | Actinobacteria,10,1,201,83 40 | Actinobacteria,11,0,42,75 41 | Actinobacteria,12,1,109,59 42 | Actinobacteria,13,0,51,183 43 | Actinobacteria,14,1,310,204 44 | Bacteroidetes,1,0,67,0 45 | Bacteroidetes,2,1,0,0 46 | Bacteroidetes,3,0,85,5 47 | Bacteroidetes,4,1,143,7 48 | Bacteroidetes,5,0,678,2 49 | Bacteroidetes,6,1,4829,209 50 | Bacteroidetes,7,0,74,651 51 | Bacteroidetes,8,1,169,254 52 | Bacteroidetes,9,0,106,10 53 | Bacteroidetes,10,1,73,381 54 | Bacteroidetes,11,0,30,359 55 | Bacteroidetes,12,1,51,51 56 | Bacteroidetes,13,0,2473,2314 57 | Bacteroidetes,14,1,102,33 58 | Other,1,0,195,18 59 | Other,2,1,42,2 60 | Other,3,0,316,43 61 | Other,4,1,202,40 62 | Other,5,0,116,0 63 | Other,6,1,527,12 64 | Other,7,0,357,11 65 | Other,8,1,106,11 66 | Other,9,0,67,14 67 | Other,10,1,203,6 68 | Other,11,0,392,6 69 | Other,12,1,28,25 70 | Other,13,0,12,22 71 | Other,14,1,305,32 -------------------------------------------------------------------------------- /data/nyc_taxi.csv: -------------------------------------------------------------------------------- 1 | ,date,rides,max_temp,precip,snow 2 | 0,2016-01-01,286750,42,0.0,0.0 3 | 1,2016-01-02,241760,40,0.0,0.0 4 | 2,2016-01-03,231116,45,0.0,0.0 5 | 3,2016-01-04,259891,36,0.0,0.0 6 | 4,2016-01-05,278457,29,0.0,0.0 7 | 5,2016-01-06,267690,41,0.0,0.0 8 | 6,2016-01-07,272341,46,0.0,0.0 9 | 7,2016-01-08,298670,46,0.0,0.0 10 | 8,2016-01-09,291508,47,0.01,0.0 11 | 9,2016-01-10,277687,59,1.8,0.0 12 | 10,2016-01-11,269529,40,0.0,0.0 13 | 11,2016-01-12,274021,44,0.0,0.01 14 | 12,2016-01-13,304574,30,0.0,0.0 15 | 13,2016-01-14,308592,38,0.0,0.01 16 | 14,2016-01-15,318552,51,0.01,0.0 17 | 15,2016-01-16,308690,52,0.24,0.0 18 | 16,2016-01-17,296477,42,0.05,0.4 19 | 17,2016-01-18,256219,31,0.01,0.01 20 | 18,2016-01-19,317835,28,0.0,0.0 21 | 19,2016-01-20,300062,37,0.0,0.0 22 | 20,2016-01-21,319814,36,0.0,0.0 23 | 21,2016-01-22,353525,30,0.01,0.2 24 | 22,2016-01-23,84680,27,2.31,27.3 25 | 23,2016-01-24,153319,35,0.01,0.01 26 | 24,2016-01-25,272528,39,0.0,0.0 27 | 25,2016-01-26,294591,48,0.0,0.0 28 | 26,2016-01-27,299536,47,0.01,0.0 29 | 27,2016-01-28,314311,42,0.0,0.0 30 | 28,2016-01-29,354874,41,0.0,0.0 31 | 29,2016-01-30,356005,39,0.0,0.0 32 | 30,2016-01-31,293122,56,0.0,0.0 33 | 31,2016-02-01,268872,59,0.01,0.0 34 | 32,2016-02-02,275915,50,0.0,0.0 35 | 33,2016-02-03,321971,59,0.73,0.0 36 | 34,2016-02-04,312912,59,0.01,0.0 37 | 35,2016-02-05,350825,44,0.53,2.5 38 | 36,2016-02-06,340393,40,0.0,0.0 39 | 37,2016-02-07,304803,47,0.0,0.0 40 | 38,2016-02-08,286167,39,0.05,0.1 41 | 39,2016-02-09,291792,36,0.0,0.01 42 | 40,2016-02-10,315306,39,0.01,0.01 43 | 41,2016-02-11,369519,31,0.01,0.01 44 | 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10,80,20,1,19.83333333,NA,NA,NA,1,0,1400,NA,NA,NA,Raven,90,NA,NA,NA,NA,NA 12 | 11,80,21,1,13.5,NA,NA,NA,1,0,1691,NA,NA,NA,Raven,96,NA,NA,NA,NA,NA 13 | 12,1054,34,31,29,NA,18,40,0,0,399.3,163.3,208.12,686.1,HAWIE,107.5,18.7,64,148,-0.463,0.01 14 | 13,1054,35,31,29,NA,18,40,0,1,308.6,102.2,181.5,570.5,HAWIE,107.5,18.7,64,148,-0.519,0.01 15 | 14,1420,37,1,26,NA,NA,NA,0,1,372,NA,NA,NA,NA,82,NA,NA,NA,NA,NA 16 | 15,1420,38,1,23,NA,NA,NA,0,1,336,NA,NA,NA,NA,84,NA,NA,NA,NA,NA 17 | 16,1420,39,1,27,NA,NA,NA,0,1,378,NA,NA,NA,NA,119,NA,NA,NA,NA,NA 18 | 17,1420,40,1,30,NA,NA,NA,0,1,390,NA,NA,NA,NA,97,NA,NA,NA,NA,NA 19 | 18,1420,41,1,30,NA,NA,NA,0,1,576,NA,NA,NA,NA,79,NA,NA,NA,NA,NA 20 | 19,1420,42,1,29,NA,NA,NA,0,1,510,NA,NA,NA,NA,124,NA,NA,NA,NA,NA 21 | 20,1420,43,1,35,NA,NA,NA,0,1,570,NA,NA,NA,NA,74,NA,NA,NA,NA,NA 22 | 21,1420,44,1,24,NA,NA,NA,0,1,462,NA,NA,NA,NA,105,NA,NA,NA,NA,NA 23 | 22,1420,45,1,33,NA,NA,NA,0,1,282,NA,NA,NA,NA,109,NA,NA,NA,NA,NA 24 | 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37,1420,41,1,30,NA,NA,NA,1,0,1152,NA,NA,NA,NA,79,NA,NA,NA,NA,NA 39 | 38,1420,42,1,29,NA,NA,NA,1,0,408,NA,NA,NA,NA,124,NA,NA,NA,NA,NA 40 | 39,1420,43,1,35,NA,NA,NA,1,0,1086,NA,NA,NA,NA,74,NA,NA,NA,NA,NA 41 | 40,1420,44,1,24,NA,NA,NA,1,0,1320,NA,NA,NA,NA,105,NA,NA,NA,NA,NA 42 | 41,1420,45,1,33,NA,NA,NA,1,0,1584,NA,NA,NA,NA,109,NA,NA,NA,NA,NA 43 | 42,1420,46,1,31,NA,NA,NA,1,0,1140,NA,NA,NA,NA,115,NA,NA,NA,NA,NA 44 | 43,1185,47,1,NA,NA,17,24,1,0,1146,NA,NA,NA,Weschler,112,NA,NA,NA,NA,NA 45 | 44,1185,48,1,NA,NA,17,24,1,0,1140,NA,NA,NA,Weschler,132,NA,NA,NA,NA,NA 46 | 45,1185,49,1,NA,NA,17,24,1,0,1524,NA,NA,NA,Weschler,109,NA,NA,NA,NA,NA 47 | 47,1185,51,1,NA,NA,17,24,1,0,900,NA,NA,NA,Weschler,106,NA,NA,NA,NA,NA 48 | 48,1185,52,1,NA,NA,17,24,1,0,1260,NA,NA,NA,Weschler,107,NA,NA,NA,NA,NA 49 | 49,1185,53,1,NA,NA,17,24,1,0,1602,NA,NA,NA,Weschler,114,NA,NA,NA,NA,NA 50 | 50,1185,54,1,NA,NA,17,24,1,0,1482,NA,NA,NA,Weschler,105,NA,NA,NA,NA,NA 51 | 51,1185,55,1,NA,NA,17,24,1,0,1524,NA,NA,NA,Weschler,108,NA,NA,NA,NA,NA 52 | 52,1185,56,1,NA,NA,17,24,1,0,1008,NA,NA,NA,Weschler,102,NA,NA,NA,NA,NA 53 | 53,1185,57,1,NA,NA,17,24,1,0,744,NA,NA,NA,Weschler,114,NA,NA,NA,NA,NA 54 | 54,1185,58,1,NA,NA,17,24,1,0,1122,NA,NA,NA,Weschler,112,NA,NA,NA,NA,NA 55 | 55,1185,59,1,NA,NA,17,24,1,0,1782,NA,NA,NA,Weschler,93,NA,NA,NA,NA,NA 56 | 56,1185,60,1,NA,NA,17,24,1,0,1518,NA,NA,NA,Weschler,126,NA,NA,NA,NA,NA 57 | 57,1185,61,1,NA,NA,17,24,1,0,996,NA,NA,NA,Weschler,104,NA,NA,NA,NA,NA 58 | 58,1185,62,1,NA,NA,17,24,1,0,720,NA,NA,NA,Weschler,126,NA,NA,NA,NA,NA 59 | 59,1185,63,1,NA,NA,17,24,1,0,564,NA,NA,NA,Weschler,111,NA,NA,NA,NA,NA 60 | 60,1185,64,1,NA,NA,17,24,1,0,1932,NA,NA,NA,Weschler,89,NA,NA,NA,NA,NA 61 | 61,229,65,1,11,NA,NA,NA,1,0,1500,NA,NA,NA,Multiple,104,NA,NA,NA,NA,NA 62 | 62,229,66,1,11,NA,NA,NA,1,0,1260,NA,NA,NA,Multiple,105,NA,NA,NA,NA,NA 63 | 63,229,67,1,12,NA,NA,NA,1,0,1680,NA,NA,NA,Multiple,101,NA,NA,NA,NA,NA 64 | 64,229,68,1,11,NA,NA,NA,1,0,1500,NA,NA,NA,Multiple,118,NA,NA,NA,NA,NA 65 | 65,229,69,1,12,NA,NA,NA,1,0,2160,NA,NA,NA,Multiple,108,NA,NA,NA,NA,NA 66 | 66,229,70,1,10,NA,NA,NA,1,0,2700,NA,NA,NA,Multiple,117,NA,NA,NA,NA,NA 67 | 67,229,71,1,12,NA,NA,NA,1,0,1740,NA,NA,NA,Multiple,101,NA,NA,NA,NA,NA 68 | 68,229,72,1,11,NA,NA,NA,1,0,1380,NA,NA,NA,Multiple,103,NA,NA,NA,NA,NA 69 | 69,229,73,1,11,NA,NA,NA,1,0,2640,NA,NA,NA,Multiple,109,NA,NA,NA,NA,NA 70 | 70,229,74,1,11,NA,NA,NA,1,0,1800,NA,NA,NA,Multiple,105,NA,NA,NA,NA,NA 71 | 71,229,75,1,12,NA,NA,NA,1,0,1320,NA,NA,NA,Multiple,110,NA,NA,NA,NA,NA 72 | 72,229,76,1,10,NA,NA,NA,1,0,1860,NA,NA,NA,Multiple,110,NA,NA,NA,NA,NA 73 | 73,229,77,1,11,NA,NA,NA,1,0,2760,NA,NA,NA,Multiple,101,NA,NA,NA,NA,NA 74 | 74,229,78,1,10,NA,NA,NA,1,0,3240,NA,NA,NA,Multiple,112,NA,NA,NA,NA,NA 75 | 75,229,79,1,11,NA,NA,NA,1,0,1680,NA,NA,NA,Multiple,100,NA,NA,NA,NA,NA 76 | 76,229,80,1,12,NA,NA,NA,1,0,2820,NA,NA,NA,Multiple,106,NA,NA,NA,NA,NA 77 | 77,229,65,1,11,NA,NA,NA,1,0,1440,NA,NA,NA,Multiple,103,NA,NA,NA,NA,NA 78 | 78,229,66,1,11,NA,NA,NA,1,0,1380,NA,NA,NA,Multiple,101,NA,NA,NA,NA,NA 79 | 79,229,67,1,12,NA,NA,NA,1,0,1560,NA,NA,NA,Multiple,98,NA,NA,NA,NA,NA 80 | 80,229,68,1,11,NA,NA,NA,1,0,1260,NA,NA,NA,Multiple,109,NA,NA,NA,NA,NA 81 | 81,229,69,1,12,NA,NA,NA,1,0,1740,NA,NA,NA,Multiple,100,NA,NA,NA,NA,NA 82 | 82,229,70,1,10,NA,NA,NA,1,0,1680,NA,NA,NA,Multiple,110,NA,NA,NA,NA,NA 83 | 83,229,71,1,12,NA,NA,NA,1,0,1680,NA,NA,NA,Multiple,102,NA,NA,NA,NA,NA 84 | 84,229,72,1,11,NA,NA,NA,1,0,1320,NA,NA,NA,Multiple,100,NA,NA,NA,NA,NA 85 | 85,229,73,1,11,NA,NA,NA,1,0,3360,NA,NA,NA,Multiple,102,NA,NA,NA,NA,NA 86 | 86,229,74,1,11,NA,NA,NA,1,0,2220,NA,NA,NA,Multiple,103,NA,NA,NA,NA,NA 87 | 87,229,75,1,12,NA,NA,NA,1,0,1440,NA,NA,NA,Multiple,110,NA,NA,NA,NA,NA 88 | 88,229,76,1,10,NA,NA,NA,1,0,1260,NA,NA,NA,Multiple,108,NA,NA,NA,NA,NA 89 | 89,229,77,1,11,NA,NA,NA,1,0,1380,NA,NA,NA,Multiple,99,NA,NA,NA,NA,NA 90 | 90,229,78,1,10,NA,NA,NA,1,0,1620,NA,NA,NA,Multiple,100,NA,NA,NA,NA,NA 91 | 91,229,79,1,11,NA,NA,NA,1,0,1680,NA,NA,NA,Multiple,96,NA,NA,NA,NA,NA 92 | 92,229,80,1,12,NA,NA,NA,1,0,1380,NA,NA,NA,Multiple,106,NA,NA,NA,NA,NA 93 | 93,494,104,57,8.14,0.3,NA,NA,0,1,466,154,NA,NA,Weschler,85.82,13.93,NA,NA,-0.35,0.01 94 | 94,633,105,1,7.5,NA,NA,NA,1,0,649,NA,NA,NA,Multiple,106,NA,NA,NA,NA,NA 95 | 95,633,106,1,7.5,NA,NA,NA,1,0,348,NA,NA,NA,Multiple,101,NA,NA,NA,NA,NA 96 | 96,633,107,1,8,NA,NA,NA,1,0,571,NA,NA,NA,Multiple,114,NA,NA,NA,NA,NA 97 | 97,633,108,1,8.5,NA,NA,NA,1,0,722,NA,NA,NA,Multiple,118,NA,NA,NA,NA,NA 98 | 98,633,109,1,8.8,NA,NA,NA,1,0,915,NA,NA,NA,Multiple,90,NA,NA,NA,NA,NA 99 | 99,633,110,1,9.2,NA,NA,NA,1,0,1166,NA,NA,NA,Multiple,114,NA,NA,NA,NA,NA 100 | 100,633,111,1,11,NA,NA,NA,1,0,642,NA,NA,NA,Multiple,119,NA,NA,NA,NA,NA 101 | 101,633,112,1,11,NA,NA,NA,1,0,1320,NA,NA,NA,Multiple,74,NA,NA,NA,NA,NA 102 | 102,633,113,1,13.5,NA,NA,NA,1,0,1035,NA,NA,NA,Multiple,115,NA,NA,NA,NA,NA 103 | 103,633,114,1,14,NA,NA,NA,1,0,1174,NA,NA,NA,Multiple,102,NA,NA,NA,NA,NA 104 | 104,633,115,1,16,NA,NA,NA,1,0,1283,NA,NA,NA,Multiple,113,NA,NA,NA,NA,NA 105 | 105,633,116,1,16,NA,NA,NA,1,0,1639,NA,NA,NA,Multiple,85,NA,NA,NA,NA,NA 106 | 106,633,117,1,17,NA,NA,NA,1,0,707,NA,NA,NA,Multiple,67,NA,NA,NA,NA,NA 107 | 107,633,118,1,17,NA,NA,NA,1,0,1611,NA,NA,NA,Multiple,80,NA,NA,NA,NA,NA 108 | 108,633,119,1,18,NA,NA,NA,1,0,1718,NA,NA,NA,Multiple,70,NA,NA,NA,NA,NA 109 | 109,633,120,1,18,NA,NA,NA,1,0,1580,NA,NA,NA,Multiple,77,NA,NA,NA,NA,NA 110 | 110,633,121,1,19,NA,NA,NA,1,0,1735,NA,NA,NA,Multiple,127,NA,NA,NA,NA,NA 111 | 111,633,122,1,19,NA,NA,NA,1,0,1410,NA,NA,NA,Multiple,99,NA,NA,NA,NA,NA 112 | 112,633,123,1,20,NA,NA,NA,1,0,1569,NA,NA,NA,Multiple,105,NA,NA,NA,NA,NA 113 | 113,633,124,1,21,NA,NA,NA,1,0,1364,NA,NA,NA,Multiple,114,NA,NA,NA,NA,NA 114 | 114,633,125,1,22,NA,NA,NA,1,0,1154,NA,NA,NA,Multiple,77,NA,NA,NA,NA,NA 115 | 115,633,126,1,22,NA,NA,NA,1,0,1186,NA,NA,NA,Multiple,114,NA,NA,NA,NA,NA 116 | 116,633,127,1,22,NA,NA,NA,1,0,959,NA,NA,NA,Multiple,44,NA,NA,NA,NA,NA 117 | 117,633,128,1,23,NA,NA,NA,1,0,1333,NA,NA,NA,Multiple,106,NA,NA,NA,NA,NA 118 | 118,633,129,1,23,NA,NA,NA,1,0,430,NA,NA,NA,Multiple,79,NA,NA,NA,NA,NA 119 | 119,633,130,1,24,NA,NA,NA,1,0,1258,NA,NA,NA,Multiple,65,NA,NA,NA,NA,NA 120 | 120,633,131,1,24,NA,NA,NA,1,0,1635,NA,NA,NA,Multiple,93,NA,NA,NA,NA,NA 121 | 121,633,132,1,25,NA,NA,NA,1,0,1208,NA,NA,NA,Multiple,97,NA,NA,NA,NA,NA 122 | 122,633,133,1,25,NA,NA,NA,1,0,2010,NA,NA,NA,Multiple,82,NA,NA,NA,NA,NA 123 | 123,633,134,1,25,NA,NA,NA,1,0,1814,NA,NA,NA,Multiple,44,NA,NA,NA,NA,NA 124 | 124,633,135,1,26,NA,NA,NA,1,0,1157,NA,NA,NA,Multiple,68,NA,NA,NA,NA,NA 125 | 125,633,136,1,29,NA,NA,NA,1,0,883,NA,NA,NA,Multiple,82,NA,NA,NA,NA,NA 126 | 126,1119,153,1,NA,NA,18,26,1,0,993,NA,NA,NA,Weschler,76,NA,NA,NA,NA,NA 127 | 127,1119,154,1,NA,NA,18,26,1,0,1102,NA,NA,NA,Weschler,99,NA,NA,NA,NA,NA 128 | 128,1119,155,1,NA,NA,18,26,1,0,1665,NA,NA,NA,Weschler,99,NA,NA,NA,NA,NA 129 | 129,1119,156,1,NA,NA,18,26,1,0,1968,NA,NA,NA,Weschler,71,NA,NA,NA,NA,NA 130 | 130,1119,157,1,NA,NA,18,26,1,0,1380,NA,NA,NA,Weschler,86,NA,NA,NA,NA,NA 131 | 131,1119,158,1,NA,NA,18,26,1,0,1538,NA,NA,NA,Weschler,88,NA,NA,NA,NA,NA 132 | 132,1119,159,1,NA,NA,18,26,1,0,920,NA,NA,NA,Weschler,81,NA,NA,NA,NA,NA 133 | 133,1119,160,1,NA,NA,18,26,1,0,1162,NA,NA,NA,Weschler,82,NA,NA,NA,NA,NA 134 | 134,1119,161,1,NA,NA,18,26,1,0,1120,NA,NA,NA,Weschler,76,NA,NA,NA,NA,NA 135 | 135,1119,162,1,NA,NA,18,26,1,0,1483,NA,NA,NA,Weschler,83,NA,NA,NA,NA,NA 136 | 136,1119,163,1,NA,NA,18,26,1,0,1084,NA,NA,NA,Weschler,92,NA,NA,NA,NA,NA 137 | 137,1119,164,1,NA,NA,18,26,1,0,1635,NA,NA,NA,Weschler,86,NA,NA,NA,NA,NA 138 | 138,1119,165,1,NA,NA,18,26,1,0,1659,NA,NA,NA,Weschler,94,NA,NA,NA,NA,NA 139 | 139,1119,166,1,NA,NA,18,26,1,0,1804,NA,NA,NA,Weschler,106,NA,NA,NA,NA,NA 140 | 140,1119,167,1,NA,NA,18,26,1,0,503,NA,NA,NA,Weschler,119,NA,NA,NA,NA,NA 141 | 141,1119,168,1,NA,NA,18,26,1,0,1399,NA,NA,NA,Weschler,87,NA,NA,NA,NA,NA 142 | 142,1119,169,1,NA,NA,18,26,1,0,254,NA,NA,NA,Weschler,96,NA,NA,NA,NA,NA 143 | 143,1119,170,1,NA,NA,18,26,1,0,1586,NA,NA,NA,Weschler,101,NA,NA,NA,NA,NA 144 | 144,1119,171,1,NA,NA,18,26,1,0,2252,NA,NA,NA,Weschler,76,NA,NA,NA,NA,NA 145 | 145,1119,172,1,NA,NA,18,26,1,0,1164,NA,NA,NA,Weschler,90,NA,NA,NA,NA,NA 146 | 146,1119,173,1,NA,NA,18,26,1,0,1284,NA,NA,NA,Weschler,98,NA,NA,NA,NA,NA 147 | 147,1119,174,1,NA,NA,18,26,1,0,1810,NA,NA,NA,Weschler,76,NA,NA,NA,NA,NA 148 | 148,1119,175,1,NA,NA,18,26,1,0,1011,NA,NA,NA,Weschler,90,NA,NA,NA,NA,NA 149 | 149,1119,176,1,NA,NA,18,26,1,0,1368,NA,NA,NA,Weschler,100,NA,NA,NA,NA,NA 150 | 150,1119,177,1,NA,NA,18,26,1,0,938,NA,NA,NA,Weschler,93,NA,NA,NA,NA,NA 151 | 151,1156,178,1,23,NA,NA,NA,1,0,1010,NA,NA,NA,WAIS-R,121,NA,NA,NA,NA,NA 152 | 152,1156,179,1,18,NA,NA,NA,1,0,1010,NA,NA,NA,WAIS-R,120,NA,NA,NA,NA,NA 153 | 153,1156,180,1,24,NA,NA,NA,1,0,900,NA,NA,NA,WAIS-R,106,NA,NA,NA,NA,NA 154 | 154,1156,181,1,21,NA,NA,NA,1,0,790,NA,NA,NA,WAIS-R,106,NA,NA,NA,NA,NA 155 | 155,1156,182,1,22,NA,NA,NA,1,0,1000,NA,NA,NA,WAIS-R,110,NA,NA,NA,NA,NA 156 | 156,1156,183,1,22,NA,NA,NA,1,0,1360,NA,NA,NA,WAIS-R,61,NA,NA,NA,NA,NA 157 | 157,1156,184,1,24,NA,NA,NA,1,0,830,NA,NA,NA,WAIS-R,112,NA,NA,NA,NA,NA 158 | 158,1156,185,1,23,NA,NA,NA,1,0,1480,NA,NA,NA,WAIS-R,100,NA,NA,NA,NA,NA 159 | 159,1156,186,1,24,NA,NA,NA,1,0,1360,NA,NA,NA,WAIS-R,86,NA,NA,NA,NA,NA 160 | 160,1156,190,1,23,NA,NA,NA,1,0,1780,NA,NA,NA,WAIS-R,93,NA,NA,NA,NA,NA 161 | 161,1156,191,1,22,NA,NA,NA,1,0,1190,NA,NA,NA,WAIS-R,95,NA,NA,NA,NA,NA 162 | 162,1156,192,1,18,NA,NA,NA,1,0,1120,NA,NA,NA,WAIS-R,114,NA,NA,NA,NA,NA 163 | 163,1156,193,1,27,NA,NA,NA,1,0,1180,NA,NA,NA,WAIS-R,103,NA,NA,NA,NA,NA 164 | 164,1156,194,1,24,NA,NA,NA,1,0,660,NA,NA,NA,WAIS-R,129,NA,NA,NA,NA,NA 165 | 165,1156,195,1,23,NA,NA,NA,1,0,1140,NA,NA,NA,WAIS-R,110,NA,NA,NA,NA,NA 166 | 166,1156,196,1,23,NA,NA,NA,1,0,1410,NA,NA,NA,WAIS-R,109,NA,NA,NA,NA,NA 167 | 167,1156,197,1,17,NA,NA,NA,1,0,1770,NA,NA,NA,WAIS-R,94,NA,NA,NA,NA,NA 168 | 168,1156,178,1,23,NA,NA,NA,0,0,480,NA,NA,NA,WAIS-R,121,NA,NA,NA,NA,NA 169 | 169,1156,179,1,18,NA,NA,NA,0,0,500,NA,NA,NA,WAIS-R,120,NA,NA,NA,NA,NA 170 | 170,1156,180,1,24,NA,NA,NA,0,0,530,NA,NA,NA,WAIS-R,106,NA,NA,NA,NA,NA 171 | 171,1156,181,1,21,NA,NA,NA,0,0,520,NA,NA,NA,WAIS-R,106,NA,NA,NA,NA,NA 172 | 172,1156,182,1,22,NA,NA,NA,0,0,560,NA,NA,NA,WAIS-R,110,NA,NA,NA,NA,NA 173 | 173,1156,183,1,22,NA,NA,NA,0,0,890,NA,NA,NA,WAIS-R,61,NA,NA,NA,NA,NA 174 | 174,1156,184,1,24,NA,NA,NA,0,0,470,NA,NA,NA,WAIS-R,112,NA,NA,NA,NA,NA 175 | 175,1156,185,1,23,NA,NA,NA,0,0,660,NA,NA,NA,WAIS-R,100,NA,NA,NA,NA,NA 176 | 176,1156,186,1,24,NA,NA,NA,0,0,650,NA,NA,NA,WAIS-R,86,NA,NA,NA,NA,NA 177 | 177,1156,190,1,23,NA,NA,NA,0,0,820,NA,NA,NA,WAIS-R,93,NA,NA,NA,NA,NA 178 | 178,1156,191,1,22,NA,NA,NA,0,0,900,NA,NA,NA,WAIS-R,95,NA,NA,NA,NA,NA 179 | 179,1156,192,1,18,NA,NA,NA,0,0,920,NA,NA,NA,WAIS-R,114,NA,NA,NA,NA,NA 180 | 180,1156,193,1,27,NA,NA,NA,0,0,780,NA,NA,NA,WAIS-R,103,NA,NA,NA,NA,NA 181 | 181,1156,194,1,24,NA,NA,NA,0,0,420,NA,NA,NA,WAIS-R,129,NA,NA,NA,NA,NA 182 | 182,1156,195,1,23,NA,NA,NA,0,0,540,NA,NA,NA,WAIS-R,110,NA,NA,NA,NA,NA 183 | 183,1156,196,1,23,NA,NA,NA,0,0,520,NA,NA,NA,WAIS-R,109,NA,NA,NA,NA,NA 184 | 184,1156,197,1,17,NA,NA,NA,0,0,970,NA,NA,NA,WAIS-R,94,NA,NA,NA,NA,NA 185 | 185,1222,198,1,13,NA,NA,NA,0,1,NA,NA,1320,1800,Multiple,82,NA,NA,NA,NA,NA 186 | 186,1222,199,1,10.75,NA,NA,NA,0,1,NA,NA,1200,2160,Multiple,95,NA,NA,NA,NA,NA 187 | 187,1222,200,1,8.25,NA,NA,NA,0,1,NA,NA,1140,2280,Multiple,68,NA,NA,NA,NA,NA 188 | 188,1222,201,1,13.17,NA,NA,NA,0,1,NA,NA,1260,1800,Multiple,77,NA,NA,NA,NA,NA 189 | 189,1222,202,1,12.5,NA,NA,NA,0,1,1500,NA,NA,,Multiple,88,NA,NA,NA,NA,NA 190 | 190,1222,203,1,8.17,NA,NA,NA,0,1,NA,NA,1800,2280,Multiple,88,NA,NA,NA,NA,NA 191 | 191,1222,204,1,13.17,NA,NA,NA,0,1,NA,NA,1260,1920,Multiple,106,NA,NA,NA,NA,NA 192 | 192,1222,205,1,13.17,NA,NA,NA,0,1,NA,NA,1440,1560,Multiple,94,NA,NA,NA,NA,NA 193 | 193,1222,206,1,11.17,NA,NA,NA,0,1,NA,NA,840,1320,Multiple,112,NA,NA,NA,NA,NA 194 | 194,1222,207,1,8.92,NA,NA,NA,0,1,NA,NA,1320,1440,Multiple,106,NA,NA,NA,NA,NA 195 | 195,1222,208,1,9.83,NA,NA,NA,0,1,NA,NA,960,1980,Multiple,102,NA,NA,NA,NA,NA 196 | 196,1222,209,1,13.25,NA,NA,NA,0,1,NA,NA,1320,2160,Multiple,71,NA,NA,NA,NA,NA 197 | 197,1222,210,1,8.75,NA,NA,NA,0,1,1860,NA,NA,,Multiple,85,NA,NA,NA,NA,NA 198 | 198,1222,211,1,14.5,NA,NA,NA,0,1,NA,NA,1380,2520,Multiple,86,NA,NA,NA,NA,NA 199 | 199,1429,212,1,30,NA,NA,NA,0,1,880,NA,NA,NA,CFT,77,NA,NA,NA,NA,NA 200 | 200,1429,213,1,17,NA,NA,NA,0,1,810,NA,NA,NA,CFT,90,NA,NA,NA,NA,NA 201 | 201,1429,214,1,15,NA,NA,NA,0,1,660,NA,NA,NA,CFT,82,NA,NA,NA,NA,NA 202 | 202,1429,215,1,19,NA,NA,NA,0,1,230,NA,NA,NA,CFT,102,NA,NA,NA,NA,NA 203 | 203,1429,216,1,18,NA,NA,NA,0,1,370,NA,NA,NA,CFT,106,NA,NA,NA,NA,NA 204 | 204,1429,217,1,20,NA,NA,NA,0,1,700,NA,NA,NA,CFT,79,NA,NA,NA,NA,NA 205 | 205,1429,218,1,15,NA,NA,NA,0,1,420,NA,NA,NA,CFT,106,NA,NA,NA,NA,NA 206 | 206,1429,219,1,17,NA,NA,NA,0,1,640,NA,NA,NA,CFT,103,NA,NA,NA,NA,NA 207 | 207,1429,220,1,19,NA,NA,NA,0,1,530,NA,NA,NA,CFT,86,NA,NA,NA,NA,NA 208 | 208,1429,221,1,15,NA,NA,NA,0,1,590,NA,NA,NA,CFT,105,NA,NA,NA,NA,NA 209 | 209,1429,222,1,17,NA,NA,NA,0,1,320,NA,NA,NA,CFT,132,NA,NA,NA,NA,NA 210 | 210,1429,223,1,14,NA,NA,NA,0,1,380,NA,NA,NA,CFT,101,NA,NA,NA,NA,NA 211 | 211,1429,224,1,23,NA,NA,NA,0,1,620,NA,NA,NA,CFT,97,NA,NA,NA,NA,NA 212 | 212,1429,225,1,21,NA,NA,NA,0,1,340,NA,NA,NA,CFT,112,NA,NA,NA,NA,NA 213 | 213,1429,226,1,17,NA,NA,NA,0,1,300,NA,NA,NA,CFT,98,NA,NA,NA,NA,NA 214 | 214,1432,227,20,NA,NA,8.9,13.1,0,0,474,144,282.00,810.00,CFT,101.4,10.2,88,121,-0.33,NS 215 | 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1448,780.0,114.0,1 213 | 1448,504.0,120.0,1 214 | 1448,636.0,116.0,1 215 | 1448,594.0,82.0,1 216 | 1448,594.0,101.0,1 217 | 1448,294.0,115.0,1 218 | 1448,1074.0,94.0,1 219 | 1448,570.0,112.0,1 220 | 1448,450.0,93.0,1 221 | 1448,648.0,119.0,1 222 | 1448,66.0,86.0,1 223 | 1429,1420.0,77.0,0 224 | 1429,1160.0,90.0,0 225 | 1429,740.0,82.0,0 226 | 1429,510.0,102.0,0 227 | 1429,890.0,106.0,0 228 | 1429,820.0,79.0,0 229 | 1429,700.0,106.0,0 230 | 1429,960.0,103.0,0 231 | 1429,640.0,86.0,0 232 | 1429,700.0,105.0,0 233 | 1429,630.0,132.0,0 234 | 1429,760.0,101.0,0 235 | 1429,850.0,97.0,0 236 | 1429,910.0,112.0,0 237 | 1429,360.0,98.0,0 238 | 1448,552.0,114.0,0 239 | 1448,594.0,120.0,0 240 | 1448,474.0,116.0,0 241 | 1448,630.0,82.0,0 242 | 1448,684.0,101.0,0 243 | 1448,618.0,115.0,0 244 | 1448,840.0,94.0,0 245 | 1448,438.0,112.0,0 246 | 1448,498.0,93.0,0 247 | 1448,444.0,119.0,0 248 | 1448,570.0,86.0,0 249 | -------------------------------------------------------------------------------- /data/salmon.txt: -------------------------------------------------------------------------------- 1 | year recruits spawners 2 | 1 68 56 3 | 2 77 62 4 | 3 299 445 5 | 4 220 279 6 | 5 142 138 7 | 6 287 428 8 | 7 276 319 9 | 8 115 102 10 | 9 64 51 11 | 10 206 289 12 | 11 222 351 13 | 12 205 282 14 | 13 233 310 15 | 14 228 266 16 | 15 188 256 17 | 16 132 144 18 | 17 285 447 19 | 18 188 186 20 | 19 224 389 21 | 20 121 113 22 | 21 311 412 23 | 22 166 176 24 | 23 248 313 25 | 24 161 162 26 | 25 226 368 27 | 26 67 54 28 | 27 201 214 29 | 28 267 429 30 | 29 121 115 31 | 30 301 407 32 | 31 244 265 33 | 32 222 301 34 | 33 195 234 35 | 34 203 229 36 | 35 210 270 37 | 36 275 478 38 | 37 286 419 39 | 38 275 490 40 | 39 304 430 41 | 40 214 235 42 | -------------------------------------------------------------------------------- /data/taxi_rides_2016.csv: -------------------------------------------------------------------------------- 1 | 2016-01-01,286750 2 | 2016-01-02,241760 3 | 2016-01-03,231116 4 | 2016-01-04,259891 5 | 2016-01-05,278457 6 | 2016-01-06,267690 7 | 2016-01-07,272341 8 | 2016-01-08,298670 9 | 2016-01-09,291508 10 | 2016-01-10,277687 11 | 2016-01-11,269529 12 | 2016-01-12,274021 13 | 2016-01-13,304574 14 | 2016-01-14,308592 15 | 2016-01-15,318552 16 | 2016-01-16,308690 17 | 2016-01-17,296477 18 | 2016-01-18,256219 19 | 2016-01-19,317835 20 | 2016-01-20,300062 21 | 2016-01-21,319814 22 | 2016-01-22,353525 23 | 2016-01-23,84680 24 | 2016-01-24,153319 25 | 2016-01-25,272528 26 | 2016-01-26,294591 27 | 2016-01-27,299536 28 | 2016-01-28,314311 29 | 2016-01-29,354874 30 | 2016-01-30,356005 31 | 2016-01-31,293122 32 | 2016-02-01,268872 33 | 2016-02-02,275915 34 | 2016-02-03,321971 35 | 2016-02-04,312912 36 | 2016-02-05,350825 37 | 2016-02-06,340393 38 | 2016-02-07,304803 39 | 2016-02-08,286167 40 | 2016-02-09,291792 41 | 2016-02-10,315306 42 | 2016-02-11,369519 43 | 2016-02-12,391964 44 | 2016-02-13,417186 45 | 2016-02-14,388224 46 | 2016-02-15,286859 47 | 2016-02-16,312839 48 | 2016-02-17,305529 49 | 2016-02-18,332941 50 | 2016-02-19,358918 51 | 2016-02-20,345382 52 | 2016-02-21,302549 53 | 2016-02-22,281686 54 | 2016-02-23,338565 55 | 2016-02-24,358849 56 | 2016-02-25,336480 57 | 2016-02-26,366793 58 | 2016-02-27,354946 59 | 2016-02-28,299653 60 | 2016-02-29,280132 61 | 2016-03-01,287685 62 | 2016-03-02,318476 63 | 2016-03-03,336838 64 | 2016-03-04,367030 65 | 2016-03-05,351386 66 | 2016-03-06,300941 67 | 2016-03-07,278546 68 | 2016-03-08,287535 69 | 2016-03-09,296904 70 | 2016-03-10,316274 71 | 2016-03-11,343973 72 | 2016-03-12,341409 73 | 2016-03-13,286790 74 | 2016-03-14,324452 75 | 2016-03-15,295580 76 | 2016-03-16,312474 77 | 2016-03-17,326747 78 | 2016-03-18,349425 79 | 2016-03-19,346651 80 | 2016-03-20,308147 81 | 2016-03-21,283918 82 | 2016-03-22,292926 83 | 2016-03-23,305999 84 | 2016-03-24,330120 85 | 2016-03-25,326047 86 | 2016-03-26,328008 87 | 2016-03-27,295796 88 | 2016-03-28,292280 89 | 2016-03-29,291040 90 | 2016-03-30,301361 91 | 2016-03-31,299829 92 | 2016-04-01,365034 93 | 2016-04-02,370508 94 | 2016-04-03,331103 95 | 2016-04-04,344697 96 | 2016-04-05,339446 97 | 2016-04-06,340218 98 | 2016-04-07,358666 99 | 2016-04-08,387714 100 | 2016-04-09,408242 101 | 2016-04-10,327340 102 | 2016-04-11,308504 103 | 2016-04-12,330521 104 | 2016-04-13,329095 105 | 2016-04-14,353200 106 | 2016-04-15,380037 107 | 2016-04-16,379054 108 | 2016-04-17,326179 109 | 2016-04-18,300259 110 | 2016-04-19,309634 111 | 2016-04-20,328013 112 | 2016-04-21,339177 113 | 2016-04-22,352083 114 | 2016-04-23,354857 115 | 2016-04-24,305723 116 | 2016-04-25,281621 117 | 2016-04-26,310235 118 | 2016-04-27,312641 119 | 2016-04-28,336882 120 | 2016-04-29,370835 121 | 2016-04-30,360844 122 | 2016-05-01,332890 123 | 2016-05-02,311540 124 | 2016-05-03,358941 125 | 2016-05-04,362678 126 | 2016-05-05,387835 127 | 2016-05-06,444608 128 | 2016-05-07,386698 129 | 2016-05-08,344622 130 | 2016-05-09,304855 131 | 2016-05-10,323602 132 | 2016-05-11,334096 133 | 2016-05-12,360236 134 | 2016-05-13,398217 135 | 2016-05-14,393706 136 | 2016-05-15,351886 137 | 2016-05-16,321581 138 | 2016-05-17,342808 139 | 2016-05-18,349789 140 | 2016-05-19,362722 141 | 2016-05-20,381751 142 | 2016-05-21,398643 143 | 2016-05-22,325321 144 | 2016-05-23,309012 145 | 2016-05-24,334745 146 | 2016-05-25,344434 147 | 2016-05-26,358272 148 | 2016-05-27,366213 149 | 2016-05-28,353971 150 | 2016-05-29,315558 151 | 2016-05-30,274150 152 | 2016-05-31,317188 153 | 2016-06-01,336213 154 | 2016-06-02,365528 155 | 2016-06-03,394452 156 | 2016-06-04,396390 157 | 2016-06-05,341566 158 | 2016-06-06,331990 159 | 2016-06-07,354809 160 | 2016-06-08,396776 161 | 2016-06-09,382190 162 | 2016-06-10,395615 163 | 2016-06-11,396999 164 | 2016-06-12,336850 165 | 2016-06-13,310265 166 | 2016-06-14,337005 167 | 2016-06-15,358610 168 | 2016-06-16,384562 169 | 2016-06-17,391588 170 | 2016-06-18,386379 171 | 2016-06-19,337114 172 | 2016-06-20,319973 173 | 2016-06-21,343723 174 | 2016-06-22,360642 175 | 2016-06-23,380170 176 | 2016-06-24,397252 177 | 2016-06-25,402625 178 | 2016-06-26,349019 179 | 2016-06-27,343179 180 | 2016-06-28,345267 181 | 2016-06-29,344952 182 | 2016-06-30,348553 183 | 2016-07-01,376207 184 | 2016-07-02,326386 185 | 2016-07-03,303506 186 | 2016-07-04,265133 187 | 2016-07-05,308347 188 | 2016-07-06,320082 189 | 2016-07-07,350879 190 | 2016-07-08,376455 191 | 2016-07-09,374675 192 | 2016-07-10,327276 193 | 2016-07-11,309193 194 | 2016-07-12,329957 195 | 2016-07-13,363784 196 | 2016-07-14,381844 197 | 2016-07-15,400760 198 | 2016-07-16,398331 199 | 2016-07-17,348867 200 | 2016-07-18,342680 201 | 2016-07-19,340079 202 | 2016-07-20,354964 203 | 2016-07-21,377089 204 | 2016-07-22,414036 205 | 2016-07-23,412441 206 | 2016-07-24,354567 207 | 2016-07-25,359247 208 | 2016-07-26,353534 209 | 2016-07-27,364033 210 | 2016-07-28,384424 211 | 2016-07-29,418171 212 | 2016-07-30,425628 213 | 2016-07-31,355318 214 | 2016-08-01,325627 215 | 2016-08-02,336761 216 | 2016-08-03,347141 217 | 2016-08-04,364408 218 | 2016-08-05,387117 219 | 2016-08-06,390158 220 | 2016-08-07,341906 221 | 2016-08-08,313093 222 | 2016-08-09,338092 223 | 2016-08-10,360027 224 | 2016-08-11,393203 225 | 2016-08-12,423132 226 | 2016-08-13,426756 227 | 2016-08-14,365238 228 | 2016-08-15,337098 229 | 2016-08-16,360143 230 | 2016-08-17,363077 231 | 2016-08-18,376700 232 | 2016-08-19,401725 233 | 2016-08-20,398640 234 | 2016-08-21,366757 235 | 2016-08-22,318245 236 | 2016-08-23,333869 237 | 2016-08-24,350549 238 | 2016-08-25,373210 239 | 2016-08-26,401125 240 | 2016-08-27,411492 241 | 2016-08-28,369582 242 | 2016-08-29,331417 243 | 2016-08-30,339327 244 | 2016-08-31,360090 245 | 2016-09-01,382092 246 | 2016-09-02,382826 247 | 2016-09-03,380735 248 | 2016-09-04,359063 249 | 2016-09-05,306116 250 | 2016-09-06,355915 251 | 2016-09-07,370958 252 | 2016-09-08,407228 253 | 2016-09-09,452249 254 | 2016-09-10,474635 255 | 2016-09-11,393872 256 | 2016-09-12,346111 257 | 2016-09-13,366344 258 | 2016-09-14,390128 259 | 2016-09-15,403497 260 | 2016-09-16,439465 261 | 2016-09-17,453741 262 | 2016-09-18,397760 263 | 2016-09-19,346398 264 | 2016-09-20,357448 265 | 2016-09-21,359311 266 | 2016-09-22,387441 267 | 2016-09-23,426816 268 | 2016-09-24,463454 269 | 2016-09-25,391841 270 | 2016-09-26,344150 271 | 2016-09-27,369338 272 | 2016-09-28,391905 273 | 2016-09-29,411788 274 | 2016-09-30,476802 275 | 2016-10-01,437328 276 | 2016-10-02,382206 277 | 2016-10-03,314134 278 | 2016-10-04,335843 279 | 2016-10-05,376877 280 | 2016-10-06,408641 281 | 2016-10-07,423088 282 | 2016-10-08,471318 283 | 2016-10-09,415904 284 | 2016-10-10,325232 285 | 2016-10-11,340464 286 | 2016-10-12,324452 287 | 2016-10-13,404169 288 | 2016-10-14,440727 289 | 2016-10-15,464922 290 | 2016-10-16,403485 291 | 2016-10-17,342410 292 | 2016-10-18,359739 293 | 2016-10-19,390362 294 | 2016-10-20,416613 295 | 2016-10-21,487689 296 | 2016-10-22,511821 297 | 2016-10-23,415773 298 | 2016-10-24,354282 299 | 2016-10-25,393213 300 | 2016-10-26,414788 301 | 2016-10-27,494932 302 | 2016-10-28,494333 303 | 2016-10-29,529010 304 | 2016-10-30,471743 305 | 2016-10-31,374093 306 | 2016-11-01,387986 307 | 2016-11-02,388804 308 | 2016-11-03,434862 309 | 2016-11-04,459520 310 | 2016-11-05,482095 311 | 2016-11-06,395450 312 | 2016-11-07,372146 313 | 2016-11-08,365860 314 | 2016-11-09,415854 315 | 2016-11-10,418606 316 | 2016-11-11,456164 317 | 2016-11-12,491405 318 | 2016-11-13,419595 319 | 2016-11-14,356661 320 | 2016-11-15,418716 321 | 2016-11-16,388797 322 | 2016-11-17,416286 323 | 2016-11-18,443891 324 | 2016-11-19,486588 325 | 2016-11-20,452911 326 | 2016-11-21,412670 327 | 2016-11-22,431609 328 | 2016-11-23,431748 329 | 2016-11-24,369520 330 | 2016-11-25,345706 331 | 2016-11-26,389512 332 | 2016-11-27,374021 333 | 2016-11-28,372713 334 | 2016-11-29,463194 335 | 2016-11-30,453745 336 | 2016-12-01,452490 337 | 2016-12-02,500696 338 | 2016-12-03,534162 339 | 2016-12-04,457793 340 | 2016-12-05,409738 341 | 2016-12-06,466585 342 | 2016-12-07,444520 343 | 2016-12-08,484789 344 | 2016-12-09,543698 345 | 2016-12-10,572306 346 | 2016-12-11,479093 347 | 2016-12-12,420286 348 | 2016-12-13,426727 349 | 2016-12-14,461293 350 | 2016-12-15,556242 351 | 2016-12-16,580773 352 | 2016-12-17,553942 353 | 2016-12-18,465982 354 | 2016-12-19,430351 355 | 2016-12-20,432321 356 | 2016-12-21,432472 357 | 2016-12-22,432676 358 | 2016-12-23,419658 359 | 2016-12-24,390726 360 | 2016-12-25,310035 361 | 2016-12-26,285508 362 | 2016-12-27,318378 363 | 2016-12-28,351170 364 | 2016-12-29,392398 365 | 2016-12-30,426911 366 | 2016-12-31,477702 367 | -------------------------------------------------------------------------------- /data/walker.txt: -------------------------------------------------------------------------------- 1 | The Sample Walker Lake Data Sets 2 | 6 3 | Identification Number 4 | Xlocation in meter 5 | Ylocation in meter 6 | V variable, concentration in ppm 7 | U variable, concentration in ppm 8 | T variable, indicator variable 9 | 1 11 8 0. 1E31 2 10 | 2 8 30 0. 1E31 2 11 | 3 9 48 224.4 1E31 2 12 | 4 8 69 434.4 1E31 2 13 | 5 9 90 412.1 1E31 2 14 | 6 10 110 587.2 1E31 2 15 | 7 9 129 192.3 1E31 2 16 | 8 11 150 31.3 1E31 2 17 | 9 10 170 388.5 1E31 2 18 | 10 8 188 174.6 1E31 2 19 | 11 9 209 187.8 1E31 2 20 | 12 10 231 82.1 1E31 1 21 | 13 11 250 81.1 1E31 1 22 | 14 10 269 124.3 1E31 2 23 | 15 8 288 188.0 1E31 2 24 | 16 31 11 28.7 1E31 2 25 | 17 29 29 78.1 1E31 2 26 | 18 28 51 292.1 1E31 2 27 | 19 31 68 895.2 1E31 2 28 | 20 28 88 702.6 1E31 2 29 | 21 30 110 490.3 1E31 2 30 | 22 28 130 136.1 1E31 2 31 | 23 28 150 335.0 1E31 2 32 | 24 30 171 277.0 1E31 2 33 | 25 28 190 206.1 1E31 2 34 | 26 31 209 24.5 1E31 2 35 | 27 28 229 198.1 1E31 2 36 | 28 30 250 60.3 1E31 2 37 | 29 31 269 312.6 1E31 2 38 | 30 31 289 240.9 1E31 2 39 | 31 49 11 653.3 1E31 2 40 | 32 49 29 96.4 1E31 2 41 | 33 51 48 105.0 1E31 2 42 | 34 49 68 37.8 1E31 2 43 | 35 50 88 820.8 1E31 2 44 | 36 51 109 450.7 1E31 2 45 | 37 48 129 190.4 1E31 2 46 | 38 49 151 773.3 1E31 2 47 | 39 51 168 971.9 1E31 2 48 | 40 48 190 762.4 1E31 2 49 | 41 50 211 968.3 1E31 2 50 | 42 49 231 394.7 1E31 2 51 | 43 51 250 343.0 1E31 2 52 | 44 50 268 863.8 1E31 2 53 | 45 51 290 159.6 1E31 1 54 | 46 71 9 445.8 1E31 2 55 | 47 71 29 673.3 1E31 2 56 | 48 70 51 252.6 1E31 2 57 | 49 68 70 537.5 1E31 2 58 | 50 69 90 0. 1E31 2 59 | 51 68 110 329.1 1E31 2 60 | 52 68 128 646.3 1E31 2 61 | 53 69 148 616.2 1E31 2 62 | 54 69 169 761.3 1E31 2 63 | 55 70 191 918.0 1E31 2 64 | 56 69 208 97.4 1E31 1 65 | 57 69 229 0. 1E31 1 66 | 58 68 250 0. 1E31 1 67 | 59 71 268 0. 1E31 1 68 | 60 71 288 2.4 1E31 1 69 | 61 91 11 368.3 1E31 2 70 | 62 91 29 91.6 1E31 2 71 | 63 90 49 654.7 1E31 2 72 | 64 91 68 645.5 1E31 2 73 | 65 91 91 907.2 1E31 2 74 | 66 91 111 826.3 1E31 2 75 | 67 89 130 975.3 1E31 2 76 | 68 88 149 551.1 1E31 2 77 | 69 89 170 155.5 1E31 1 78 | 70 89 188 10.7 1E31 1 79 | 71 90 211 0. 1E31 1 80 | 72 90 230 0. 1E31 1 81 | 73 88 249 0. 1E31 1 82 | 74 88 269 12.1 1E31 1 83 | 75 88 288 62.2 1E31 1 84 | 76 109 11 399.6 1E31 2 85 | 77 111 31 176.6 1E31 2 86 | 78 108 49 402.0 1E31 2 87 | 79 109 68 260.6 1E31 2 88 | 80 108 88 192.0 1E31 2 89 | 81 110 109 237.6 1E31 2 90 | 82 109 129 702.0 1E31 2 91 | 83 110 148 38.5 1E31 2 92 | 84 111 169 22.1 1E31 1 93 | 85 111 191 2.7 1E31 1 94 | 86 110 208 17.9 1E31 1 95 | 87 109 230 174.2 1E31 2 96 | 88 109 249 12.9 1E31 2 97 | 89 109 268 187.8 1E31 2 98 | 90 111 291 268.8 1E31 2 99 | 91 130 9 572.5 1E31 2 100 | 92 131 31 29.1 1E31 2 101 | 93 130 48 75.2 1E31 2 102 | 94 128 70 399.9 1E31 2 103 | 95 129 90 243.1 1E31 2 104 | 96 131 109 0. 1E31 2 105 | 97 129 128 244.7 1E31 2 106 | 98 131 148 185.2 1E31 2 107 | 99 131 169 26.0 1E31 1 108 | 100 129 191 0. 1E31 1 109 | 101 128 209 100.3 1E31 1 110 | 102 130 231 530.3 1E31 2 111 | 103 131 248 107.4 1E31 2 112 | 104 128 269 159.3 1E31 2 113 | 105 131 288 70.7 1E31 2 114 | 106 148 8 260.2 1E31 2 115 | 107 149 29 326.0 1E31 2 116 | 108 150 49 332.7 1E31 2 117 | 109 151 69 531.3 1E31 2 118 | 110 150 89 547.2 1E31 2 119 | 111 150 109 482.7 1E31 2 120 | 112 150 129 84.1 1E31 2 121 | 113 150 151 4.7 1E31 2 122 | 114 149 169 180.6 1E31 2 123 | 115 151 190 0. 1E31 1 124 | 116 148 208 342.4 1E31 2 125 | 117 150 228 602.3 1E31 2 126 | 118 149 251 209.1 1E31 2 127 | 119 149 271 79.4 1E31 2 128 | 120 148 291 104.1 1E31 2 129 | 121 168 8 446.0 1E31 2 130 | 122 171 29 189.9 1E31 2 131 | 123 169 49 280.4 1E31 2 132 | 124 168 69 0. 1E31 1 133 | 125 168 91 499.3 1E31 2 134 | 126 171 109 457.3 1E31 2 135 | 127 168 131 341.2 1E31 2 136 | 128 171 150 0. 1E31 2 137 | 129 171 171 208.3 1E31 2 138 | 130 169 191 99.7 1E31 1 139 | 131 170 210 636.6 1E31 2 140 | 132 170 230 173.1 1E31 2 141 | 133 169 249 17.0 1E31 2 142 | 134 168 271 283.1 1E31 2 143 | 135 168 290 30.9 1E31 1 144 | 136 190 11 348.5 1E31 2 145 | 137 191 28 222.4 1E31 2 146 | 138 191 48 59.1 1E31 2 147 | 139 190 69 0. 1E31 1 148 | 140 190 89 326.0 1E31 2 149 | 141 188 111 325.1 1E31 2 150 | 142 191 129 114.7 1E31 2 151 | 143 189 149 481.6 1E31 2 152 | 144 190 169 324.1 1E31 2 153 | 145 190 189 10.9 1E31 1 154 | 146 188 210 332.9 1E31 2 155 | 147 191 231 184.4 1E31 2 156 | 148 190 248 146.6 1E31 2 157 | 149 189 270 92.0 1E31 1 158 | 150 189 290 2.5 1E31 1 159 | 151 211 11 358.1 1E31 2 160 | 152 209 30 473.3 1E31 2 161 | 153 211 49 308.8 1E31 2 162 | 154 210 70 406.8 1E31 2 163 | 155 209 90 812.1 1E31 2 164 | 156 210 111 339.7 1E31 2 165 | 157 211 130 223.9 1E31 2 166 | 158 208 151 673.5 1E31 2 167 | 159 209 168 141.0 1E31 2 168 | 160 208 191 61.8 1E31 1 169 | 161 210 211 258.3 1E31 2 170 | 162 211 228 590.3 1E31 2 171 | 163 211 250 166.9 1E31 2 172 | 164 208 268 125.2 1E31 2 173 | 165 208 289 29.3 1E31 1 174 | 166 231 10 617.6 1E31 2 175 | 167 231 28 425.9 1E31 2 176 | 168 230 50 295.7 1E31 2 177 | 169 230 71 224.9 1E31 2 178 | 170 229 91 31.7 1E31 1 179 | 171 229 110 377.4 1E31 2 180 | 172 230 131 333.3 1E31 2 181 | 173 228 148 351.0 1E31 2 182 | 174 229 169 0. 1E31 1 183 | 175 231 191 137.6 1E31 2 184 | 176 231 208 451.2 1E31 2 185 | 177 229 228 639.5 1E31 2 186 | 178 231 249 119.9 1E31 2 187 | 179 231 268 27.2 1E31 1 188 | 180 230 291 2.1 1E31 1 189 | 181 249 9 167.7 1E31 2 190 | 182 250 30 147.8 1E31 2 191 | 183 249 48 442.7 1E31 2 192 | 184 251 69 487.7 1E31 2 193 | 185 251 91 0. 1E31 1 194 | 186 248 109 28.2 1E31 1 195 | 187 249 130 0. 1E31 1 196 | 188 248 150 18.3 1E31 1 197 | 189 250 169 266.3 1E31 2 198 | 190 250 190 502.3 1E31 2 199 | 191 251 208 0. 1E31 2 200 | 192 251 229 240.9 1E31 2 201 | 193 249 251 234.4 1E31 2 202 | 194 248 270 22.4 1E31 1 203 | 195 250 291 45.6 1E31 1 204 | 196 40 71 76.2 1.1 2 205 | 197 21 69 284.3 7.8 2 206 | 198 28 80 606.8 105.3 2 207 | 199 29 59 772.7 1512.7 2 208 | 200 41 81 269.5 9.8 2 209 | 201 18 80 1036.7 860.4 2 210 | 202 39 60 238.6 12.7 2 211 | 203 18 60 519.4 177.1 2 212 | 204 41 90 414.9 23.4 2 213 | 205 21 90 601.4 173.1 2 214 | 206 31 101 579.2 296.5 2 215 | 207 41 100 601.4 300.6 2 216 | 208 21 100 594.6 229.7 2 217 | 209 60 8 550.1 258.3 2 218 | 210 40 11 99.4 2.2 2 219 | 211 51 18 233.6 14.2 2 220 | 212 59 20 14.4 0.1 2 221 | 213 41 21 115.9 3.1 2 222 | 214 59 90 506.2 126.9 2 223 | 215 51 101 502.4 73.8 2 224 | 216 50 81 608.0 210.7 2 225 | 217 59 101 363.9 30.4 2 226 | 218 60 81 385.6 50.3 2 227 | 219 60 151 1521.1 3691.8 2 228 | 220 38 148 340.9 50.0 2 229 | 221 50 160 879.1 474.2 2 230 | 222 50 138 413.4 83.0 2 231 | 223 61 158 868.9 983.8 2 232 | 224 39 160 657.4 217.8 2 233 | 225 61 139 477.0 71.5 2 234 | 226 38 140 268.5 26.2 2 235 | 227 61 170 806.4 301.1 2 236 | 228 39 170 914.4 1548.5 2 237 | 229 49 179 811.5 234.9 2 238 | 230 58 179 1113.6 2154.7 2 239 | 231 39 181 1008.0 3637.4 2 240 | 232 60 191 1528.1 1930.9 2 241 | 233 40 190 970.9 1391.1 2 242 | 234 51 198 1109.0 1660.8 2 243 | 235 60 198 1203.9 1813.7 2 244 | 236 40 200 641.3 249.1 2 245 | 237 58 208 720.6 1160.1 2 246 | 238 38 209 665.3 547.8 2 247 | 239 50 221 543.3 1066.6 2 248 | 240 61 220 101.1 59.5 1 249 | 241 39 221 615.9 420.9 2 250 | 242 59 268 543.1 1714.2 2 251 | 243 41 271 868.8 828.7 2 252 | 244 49 278 583.0 1788.8 2 253 | 245 51 260 670.7 3738.9 2 254 | 246 59 281 148.8 675.0 1 255 | 247 39 279 798.0 1182.1 2 256 | 248 59 258 194.9 983.3 1 257 | 249 38 260 635.2 766.6 2 258 | 250 78 28 781.6 565.4 2 259 | 251 60 29 238.6 12.7 2 260 | 252 70 41 472.0 84.9 2 261 | 253 70 21 58.1 0.3 2 262 | 254 78 41 600.3 124.6 2 263 | 255 61 41 64.9 0.8 2 264 | 256 78 20 505.9 70.0 2 265 | 257 80 131 801.6 421.1 2 266 | 258 58 128 158.8 4.3 2 267 | 259 71 140 606.3 175.1 2 268 | 260 70 121 30.7 0.0 2 269 | 261 79 138 730.1 1694.5 2 270 | 262 80 119 421.2 35.1 2 271 | 263 61 121 104.8 1.8 2 272 | 264 79 149 44.1 0.0 2 273 | 265 71 160 801.1 2535.0 2 274 | 266 78 159 742.0 3371.5 2 275 | 267 80 168 689.1 634.6 2 276 | 268 69 181 424.6 762.6 2 277 | 269 79 181 184.3 241.7 2 278 | 270 80 188 245.2 431.1 2 279 | 271 70 198 630.0 1992.1 2 280 | 272 81 200 0.0 0.0 1 281 | 273 100 48 48.7 0.0 2 282 | 274 80 49 757.4 473.8 2 283 | 275 90 58 739.8 280.2 2 284 | 276 88 39 520.7 76.8 2 285 | 277 100 60 0.0 0.0 2 286 | 278 80 59 0.0 0.0 2 287 | 279 101 38 730.5 464.0 2 288 | 280 101 68 383.1 97.8 2 289 | 281 79 70 508.8 103.9 2 290 | 282 90 79 573.3 138.3 2 291 | 283 100 78 372.4 70.9 2 292 | 284 81 81 585.8 197.2 2 293 | 285 100 91 397.2 38.9 2 294 | 286 80 89 614.5 192.3 2 295 | 287 91 99 734.9 159.6 2 296 | 288 101 100 599.3 539.3 2 297 | 289 81 98 181.2 1.3 2 298 | 290 98 111 744.8 1987.0 2 299 | 291 81 108 1022.3 643.0 2 300 | 292 90 120 899.3 1290.3 2 301 | 293 100 118 363.7 20.5 2 302 | 294 98 130 513.2 263.9 2 303 | 295 90 140 648.8 2147.5 2 304 | 296 90 138 645.4 1927.1 2 305 | 297 121 131 13.0 0.2 2 306 | 298 111 140 190.3 48.8 2 307 | 299 108 121 893.0 3070.9 2 308 | 300 120 141 104.7 14.6 2 309 | 301 119 118 150.4 16.9 2 310 | 302 158 228 558.4 551.6 2 311 | 303 140 229 558.0 513.9 2 312 | 304 150 241 318.5 129.2 2 313 | 305 151 218 394.3 239.2 2 314 | 306 161 241 141.9 8.6 2 315 | 307 141 240 112.5 4.6 2 316 | 308 160 218 580.4 1118.7 2 317 | 309 139 220 535.9 445.7 2 318 | 310 178 211 398.2 561.8 2 319 | 311 159 209 517.3 148.8 2 320 | 312 169 221 427.2 197.2 2 321 | 313 170 198 367.6 1429.8 2 322 | 314 180 218 374.7 77.5 2 323 | 315 178 201 144.8 81.3 2 324 | 316 158 198 169.8 154.5 2 325 | 317 219 88 235.1 136.9 2 326 | 318 198 90 611.7 735.8 2 327 | 319 211 100 746.4 710.6 2 328 | 320 208 80 436.6 495.8 2 329 | 321 221 99 540.9 586.8 2 330 | 322 199 98 801.0 1419.0 2 331 | 323 220 81 272.1 177.3 2 332 | 324 198 78 204.1 86.0 2 333 | 325 220 150 543.9 675.0 2 334 | 326 200 150 606.2 381.1 2 335 | 327 208 159 356.0 280.4 2 336 | 328 210 140 440.9 330.3 2 337 | 329 221 160 301.8 365.6 2 338 | 330 198 161 369.4 154.5 2 339 | 331 219 139 166.8 24.5 2 340 | 332 200 139 230.9 42.2 2 341 | 333 239 8 240.3 80.2 2 342 | 334 218 8 737.1 1373.4 2 343 | 335 229 19 518.6 147.7 2 344 | 336 239 18 390.7 186.7 2 345 | 337 218 18 797.4 1429.7 2 346 | 338 238 229 602.6 1510.9 2 347 | 339 218 228 430.8 265.2 2 348 | 340 231 239 354.1 478.0 2 349 | 341 230 221 602.4 538.9 2 350 | 342 240 239 172.6 51.9 2 351 | 343 221 241 324.8 290.9 2 352 | 344 239 220 420.1 398.3 2 353 | 345 218 218 763.5 1236.7 2 354 | 346 35 71 687.8 486.8 2 355 | 347 24 71 735.8 463.9 2 356 | 348 34 88 86.9 0.1 2 357 | 349 23 91 817.0 708.8 2 358 | 350 54 10 637.9 349.4 2 359 | 351 46 11 512.3 392.0 2 360 | 352 55 89 423.4 21.2 2 361 | 353 45 89 569.6 62.8 2 362 | 354 53 150 858.0 873.0 2 363 | 355 46 148 234.0 7.5 2 364 | 356 55 168 876.0 288.1 2 365 | 357 43 170 1082.8 1174.9 2 366 | 358 55 191 1392.6 1004.7 2 367 | 359 44 191 646.6 76.8 2 368 | 360 55 211 889.5 938.8 2 369 | 361 46 211 509.2 429.7 2 370 | 362 54 269 613.1 2922.4 2 371 | 363 43 271 767.8 198.1 2 372 | 364 73 29 649.4 231.7 2 373 | 365 64 31 235.4 10.8 2 374 | 366 75 129 782.8 639.3 2 375 | 367 64 129 227.3 8.6 2 376 | 368 73 149 722.9 696.1 2 377 | 369 64 151 974.5 664.1 2 378 | 370 75 171 512.2 144.8 2 379 | 371 63 168 1215.8 1446.1 2 380 | 372 73 188 687.1 2351.5 2 381 | 373 64 191 1259.9 1257.6 2 382 | 374 93 48 687.5 373.2 2 383 | 375 86 48 471.9 31.8 2 384 | 376 93 70 512.1 196.1 2 385 | 377 84 69 963.9 1210.0 2 386 | 378 93 90 874.0 1031.3 2 387 | 379 86 89 582.4 117.0 2 388 | 380 96 111 553.2 360.5 2 389 | 381 85 108 937.3 1495.5 2 390 | 382 93 131 883.6 1336.8 2 391 | 383 86 131 879.9 965.3 2 392 | 384 114 131 268.4 104.4 2 393 | 385 106 130 651.5 1957.7 2 394 | 386 155 229 386.4 88.2 2 395 | 387 145 230 333.2 63.5 2 396 | 388 174 208 339.2 335.0 2 397 | 389 166 211 600.3 647.6 2 398 | 390 215 89 595.2 1457.0 2 399 | 391 205 89 809.6 955.8 2 400 | 392 215 148 293.3 67.7 2 401 | 393 204 151 697.3 444.5 2 402 | 394 236 9 515.9 1593.8 2 403 | 395 223 9 613.2 277.6 2 404 | 396 236 229 665.3 1962.0 2 405 | 397 226 230 813.6 2279.8 2 406 | 398 35 80 174.8 2.0 2 407 | 399 24 79 891.8 635.7 2 408 | 400 36 61 699.6 1547.8 2 409 | 401 26 58 39.5 0.3 2 410 | 402 16 80 915.6 634.8 2 411 | 403 43 60 584.0 97.4 2 412 | 404 15 88 610.0 319.3 2 413 | 405 46 99 566.8 100.2 2 414 | 406 36 99 38.1 0.0 2 415 | 407 54 80 483.0 105.0 2 416 | 408 46 81 542.6 138.6 2 417 | 409 54 161 959.3 466.7 2 418 | 410 43 161 631.9 261.1 2 419 | 411 65 160 928.3 2252.5 2 420 | 412 33 160 431.0 48.6 2 421 | 413 36 170 672.3 605.5 2 422 | 414 53 179 1003.4 425.4 2 423 | 415 44 180 876.4 937.8 2 424 | 416 65 181 734.1 589.3 2 425 | 417 34 180 366.0 110.2 2 426 | 418 33 191 296.5 79.1 2 427 | 419 55 199 1069.2 376.4 2 428 | 420 46 198 804.3 674.6 2 429 | 421 63 201 731.1 1363.3 2 430 | 422 34 201 318.1 79.6 2 431 | 423 65 210 238.6 488.3 2 432 | 424 35 208 428.9 161.2 2 433 | 425 46 220 734.4 1236.1 2 434 | 426 36 219 429.1 152.3 2 435 | 427 35 217 597.4 397.0 2 436 | 428 53 258 442.6 1696.4 2 437 | 429 46 260 765.2 779.8 2 438 | 430 45 281 605.5 934.8 2 439 | 431 35 278 795.9 1588.3 2 440 | 432 35 259 235.0 18.9 2 441 | 433 84 30 562.0 85.3 2 442 | 434 84 41 411.4 34.0 2 443 | 435 75 40 696.7 356.2 2 444 | 436 73 141 790.9 607.3 2 445 | 437 63 140 696.5 357.8 2 446 | 438 84 138 687.3 893.4 2 447 | 439 76 159 597.5 997.3 2 448 | 440 84 161 437.4 387.2 2 449 | 441 86 169 317.4 761.7 2 450 | 442 73 199 470.7 5190.1 2 451 | 443 76 51 498.7 101.8 2 452 | 444 94 61 778.7 1354.0 2 453 | 445 85 60 523.3 117.5 2 454 | 446 104 38 617.1 200.2 2 455 | 447 93 41 395.5 28.4 2 456 | 448 75 90 518.9 113.0 2 457 | 449 94 101 383.7 12.9 2 458 | 450 85 100 704.1 126.0 2 459 | 451 104 109 562.3 908.6 2 460 | 452 75 110 655.3 349.0 2 461 | 453 95 121 823.6 548.4 2 462 | 454 83 119 847.7 701.4 2 463 | 455 94 140 607.5 723.2 2 464 | 456 103 139 491.2 565.3 2 465 | 457 114 120 319.5 154.2 2 466 | 458 104 118 594.0 289.2 2 467 | 459 196 91 433.5 254.1 2 468 | 460 215 101 209.6 4.0 2 469 | 461 204 101 533.8 127.3 2 470 | 462 196 101 592.4 419.4 2 471 | 463 195 149 478.7 141.9 2 472 | 464 216 11 660.2 1424.8 2 473 | 465 225 19 832.2 512.2 2 474 | 466 214 19 242.5 15.6 2 475 | 467 245 231 161.2 26.1 2 476 | 468 233 220 626.0 959.7 2 477 | 469 226 221 800.1 1681.5 2 478 | 470 213 218 482.6 476.2 2 479 | 480 | -------------------------------------------------------------------------------- /data/weather_central_park_2016.csv: -------------------------------------------------------------------------------- 1 | date,maximum temperature,minimum temperature,average temperature,precipitation,snow fall,snow depth 2 | 1-1-2016,42,34,38.0,0.00,0.0,0 3 | 2-1-2016,40,32,36.0,0.00,0.0,0 4 | 3-1-2016,45,35,40.0,0.00,0.0,0 5 | 4-1-2016,36,14,25.0,0.00,0.0,0 6 | 5-1-2016,29,11,20.0,0.00,0.0,0 7 | 6-1-2016,41,25,33.0,0.00,0.0,0 8 | 7-1-2016,46,31,38.5,0.00,0.0,0 9 | 8-1-2016,46,31,38.5,0.00,0.0,0 10 | 9-1-2016,47,40,43.5,T,0.0,0 11 | 10-1-2016,59,40,49.5,1.80,0.0,0 12 | 11-1-2016,40,26,33.0,0.00,0.0,0 13 | 12-1-2016,44,25,34.5,0.00,T,0 14 | 13-1-2016,30,22,26.0,0.00,0.0,0 15 | 14-1-2016,38,22,30.0,0.00,T,0 16 | 15-1-2016,51,34,42.5,T,0.0,0 17 | 16-1-2016,52,42,47.0,0.24,0.0,0 18 | 17-1-2016,42,30,36.0,0.05,0.4,0 19 | 18-1-2016,31,18,24.5,T,T,T 20 | 19-1-2016,28,16,22.0,0.00,0.0,T 21 | 20-1-2016,37,27,32.0,0.00,0.0,T 22 | 21-1-2016,36,26,31.0,0.00,0.0,0 23 | 22-1-2016,30,21,25.5,0.01,0.2,0 24 | 23-1-2016,27,24,25.5,2.31,27.3,6 25 | 24-1-2016,35,20,27.5,T,T,22 26 | 25-1-2016,39,28,33.5,0.00,0.0,19 27 | 26-1-2016,48,38,43.0,0.00,0.0,17 28 | 27-1-2016,47,34,40.5,T,0.0,9 29 | 28-1-2016,42,32,37.0,0.00,0.0,6 30 | 29-1-2016,41,30,35.5,0.00,0.0,6 31 | 30-1-2016,39,28,33.5,0.00,0.0,6 32 | 31-1-2016,56,36,46.0,0.00,0.0,4 33 | 1-2-2016,59,44,51.5,0.01,0.0,2 34 | 2-2-2016,50,38,44.0,0.00,0.0,T 35 | 3-2-2016,59,42,50.5,0.73,0.0,0 36 | 4-2-2016,59,44,51.5,T,0.0,0 37 | 5-2-2016,44,31,37.5,0.53,2.5,1 38 | 6-2-2016,40,30,35.0,0.00,0.0,0 39 | 7-2-2016,47,33,40.0,0.00,0.0,0 40 | 8-2-2016,39,28,33.5,0.05,0.1,0 41 | 9-2-2016,36,27,31.5,0.00,T,T 42 | 10-2-2016,39,31,35.0,0.01,T,0 43 | 11-2-2016,31,18,24.5,T,T,0 44 | 12-2-2016,27,15,21.0,0.00,0.0,0 45 | 13-2-2016,22,6,14.0,0.00,0.0,0 46 | 14-2-2016,15,-1,7.0,0.00,0.0,0 47 | 15-2-2016,35,13,24.0,0.44,1.4,0 48 | 16-2-2016,54,35,44.5,1.01,0.0,0 49 | 17-2-2016,39,35,37.0,0.00,0.0,0 50 | 18-2-2016,36,27,31.5,0.00,0.0,0 51 | 19-2-2016,39,24,31.5,0.00,0.0,0 52 | 20-2-2016,61,39,50.0,0.00,0.0,0 53 | 21-2-2016,55,44,49.5,0.03,0.0,0 54 | 22-2-2016,52,38,45.0,0.00,0.0,0 55 | 23-2-2016,40,35,37.5,0.30,T,0 56 | 24-2-2016,60,36,48.0,1.22,0.0,0 57 | 25-2-2016,61,37,49.0,0.02,0.0,0 58 | 26-2-2016,39,27,33.0,0.00,0.0,0 59 | 27-2-2016,41,26,33.5,0.00,0.0,0 60 | 28-2-2016,60,38,49.0,0.00,0.0,0 61 | 29-2-2016,61,47,54.0,0.05,0.0,0 62 | 1-3-2016,52,39,45.5,0.00,0.0,0 63 | 2-3-2016,55,29,42.0,0.14,0.0,0 64 | 3-3-2016,36,26,31.0,0.00,0.0,0 65 | 4-3-2016,39,30,34.5,0.11,0.4,T 66 | 5-3-2016,41,28,34.5,0.00,0.0,0 67 | 6-3-2016,44,32,38.0,0.00,0.0,0 68 | 7-3-2016,60,36,48.0,0.00,0.0,0 69 | 8-3-2016,67,47,57.0,0.00,0.0,0 70 | 9-3-2016,77,44,60.5,0.00,0.0,0 71 | 10-3-2016,79,63,71.0,0.00,0.0,0 72 | 11-3-2016,68,48,58.0,0.06,0.0,0 73 | 12-3-2016,59,40,49.5,0.00,0.0,0 74 | 13-3-2016,62,50,56.0,T,0.0,0 75 | 14-3-2016,51,40,45.5,0.29,0.0,0 76 | 15-3-2016,57,44,50.5,0.00,0.0,0 77 | 16-3-2016,65,48,56.5,0.02,0.0,0 78 | 17-3-2016,63,45,54.0,T,0.0,0 79 | 18-3-2016,57,42,49.5,0.00,0.0,0 80 | 19-3-2016,46,36,41.0,0.00,0.0,0 81 | 20-3-2016,43,32,37.5,0.07,T,0 82 | 21-3-2016,50,32,41.0,0.06,0.5,T 83 | 22-3-2016,56,35,45.5,0.00,0.0,0 84 | 23-3-2016,71,48,59.5,0.00,0.0,0 85 | 24-3-2016,55,44,49.5,0.00,0.0,0 86 | 25-3-2016,72,44,58.0,0.04,0.0,0 87 | 26-3-2016,55,38,46.5,0.00,0.0,0 88 | 27-3-2016,55,43,49.0,0.00,0.0,0 89 | 28-3-2016,62,42,52.0,0.38,0.0,0 90 | 29-3-2016,53,40,46.5,0.00,0.0,0 91 | 30-3-2016,56,37,46.5,0.00,0.0,0 92 | 31-3-2016,73,49,61.0,0.00,0.0,0 93 | 1-4-2016,79,61,70.0,0.02,0.0,0 94 | 2-4-2016,61,49,55.0,0.16,0.0,0 95 | 3-4-2016,50,34,42.0,0.09,T,0 96 | 4-4-2016,45,29,37.0,0.47,T,0 97 | 5-4-2016,43,26,34.5,0.00,0.0,0 98 | 6-4-2016,48,30,39.0,0.00,0.0,0 99 | 7-4-2016,58,48,53.0,0.09,0.0,0 100 | 8-4-2016,50,40,45.0,0.01,0.0,0 101 | 9-4-2016,43,36,39.5,0.11,T,0 102 | 10-4-2016,50,31,40.5,0.00,0.0,0 103 | 11-4-2016,65,43,54.0,0.01,0.0,0 104 | 12-4-2016,59,45,52.0,0.20,0.0,0 105 | 13-4-2016,58,40,49.0,0.00,0.0,0 106 | 14-4-2016,62,43,52.5,0.00,0.0,0 107 | 15-4-2016,65,42,53.5,0.00,0.0,0 108 | 16-4-2016,68,43,55.5,0.00,0.0,0 109 | 17-4-2016,75,44,59.5,0.00,0.0,0 110 | 18-4-2016,82,51,66.5,0.00,0.0,0 111 | 19-4-2016,73,55,64.0,0.00,0.0,0 112 | 20-4-2016,69,49,59.0,0.00,0.0,0 113 | 21-4-2016,73,49,61.0,0.00,0.0,0 114 | 22-4-2016,79,62,70.5,T,0.0,0 115 | 23-4-2016,71,54,62.5,0.16,0.0,0 116 | 24-4-2016,68,47,57.5,0.00,0.0,0 117 | 25-4-2016,69,50,59.5,0.00,0.0,0 118 | 26-4-2016,60,47,53.5,0.24,0.0,0 119 | 27-4-2016,62,46,54.0,0.00,0.0,0 120 | 28-4-2016,59,48,53.5,0.00,0.0,0 121 | 29-4-2016,58,45,51.5,0.05,0.0,0 122 | 30-4-2016,65,46,55.5,0.00,0.0,0 123 | 1-5-2016,51,45,48.0,0.16,0.0,0 124 | 2-5-2016,60,45,52.5,0.04,0.0,0 125 | 3-5-2016,56,51,53.5,0.61,0.0,0 126 | 4-5-2016,52,48,50.0,0.01,0.0,0 127 | 5-5-2016,57,46,51.5,0.00,0.0,0 128 | 6-5-2016,54,48,51.0,0.54,0.0,0 129 | 7-5-2016,60,48,54.0,0.00,0.0,0 130 | 8-5-2016,66,49,57.5,0.16,0.0,0 131 | 9-5-2016,72,52,62.0,0.00,0.0,0 132 | 10-5-2016,63,50,56.5,0.00,0.0,0 133 | 11-5-2016,76,50,63.0,0.00,0.0,0 134 | 12-5-2016,80,56,68.0,0.00,0.0,0 135 | 13-5-2016,65,57,61.0,0.25,0.0,0 136 | 14-5-2016,73,56,64.5,0.00,0.0,0 137 | 15-5-2016,59,46,52.5,0.00,0.0,0 138 | 16-5-2016,66,43,54.5,0.00,0.0,0 139 | 17-5-2016,64,52,58.0,0.00,0.0,0 140 | 18-5-2016,68,52,60.0,T,0.0,0 141 | 19-5-2016,73,54,63.5,0.00,0.0,0 142 | 20-5-2016,76,53,64.5,0.00,0.0,0 143 | 21-5-2016,66,54,60.0,0.04,0.0,0 144 | 22-5-2016,70,52,61.0,0.09,0.0,0 145 | 23-5-2016,78,56,67.0,0.02,0.0,0 146 | 24-5-2016,73,58,65.5,0.18,0.0,0 147 | 25-5-2016,88,61,74.5,0.00,0.0,0 148 | 26-5-2016,90,69,79.5,0.00,0.0,0 149 | 27-5-2016,87,73,80.0,0.00,0.0,0 150 | 28-5-2016,92,71,81.5,0.00,0.0,0 151 | 29-5-2016,87,70,78.5,T,0.0,0 152 | 30-5-2016,82,68,75.0,1.65,0.0,0 153 | 31-5-2016,85,71,78.0,0.00,0.0,0 154 | 1-6-2016,83,66,74.5,0.00,0.0,0 155 | 2-6-2016,78,62,70.0,0.00,0.0,0 156 | 3-6-2016,70,63,66.5,0.04,0.0,0 157 | 4-6-2016,83,66,74.5,0.40,0.0,0 158 | 5-6-2016,71,65,68.0,0.91,0.0,0 159 | 6-6-2016,83,65,74.0,0.00,0.0,0 160 | 7-6-2016,85,64,74.5,T,0.0,0 161 | 8-6-2016,67,52,59.5,0.45,0.0,0 162 | 9-6-2016,71,54,62.5,0.00,0.0,0 163 | 10-6-2016,77,57,67.0,0.00,0.0,0 164 | 11-6-2016,88,59,73.5,0.00,0.0,0 165 | 12-6-2016,83,62,72.5,0.00,0.0,0 166 | 13-6-2016,74,57,65.5,0.00,0.0,0 167 | 14-6-2016,79,58,68.5,0.00,0.0,0 168 | 15-6-2016,85,62,73.5,0.00,0.0,0 169 | 16-6-2016,74,65,69.5,0.22,0.0,0 170 | 17-6-2016,78,63,70.5,0.00,0.0,0 171 | 18-6-2016,87,61,74.0,0.00,0.0,0 172 | 19-6-2016,88,66,77.0,0.00,0.0,0 173 | 20-6-2016,84,64,74.0,0.00,0.0,0 174 | 21-6-2016,87,72,79.5,T,0.0,0 175 | 22-6-2016,86,68,77.0,0.00,0.0,0 176 | 23-6-2016,83,69,76.0,0.00,0.0,0 177 | 24-6-2016,84,67,75.5,0.00,0.0,0 178 | 25-6-2016,86,64,75.0,0.00,0.0,0 179 | 26-6-2016,87,67,77.0,0.00,0.0,0 180 | 27-6-2016,83,67,75.0,0.45,0.0,0 181 | 28-6-2016,76,68,72.0,0.12,0.0,0 182 | 29-6-2016,83,67,75.0,0.01,0.0,0 183 | 30-6-2016,85,67,76.0,0.00,0.0,0 184 | 1-7-2016,79,66,72.5,0.83,0,0 185 | 2-7-2016,76,63,69.5,0,0,0 186 | 3-7-2016,78,64,71,0,0,0 187 | 4-7-2016,84,66,75,0.49,0,0 188 | 5-7-2016,86,69,77.5,0.66,0,0 189 | 6-7-2016,91,75,83,0,0,0 190 | 7-7-2016,89,77,83,0.04,0,0 191 | 8-7-2016,86,67,76.5,0.08,0,0 192 | 9-7-2016,72,65,68.5,0.53,0,0 193 | 10-7-2016,80,66,73,T,0,0 194 | 11-7-2016,81,65,73,0,0,0 195 | 12-7-2016,82,68,75,0,0,0 196 | 13-7-2016,85,71,78,0,0,0 197 | 14-7-2016,88,73,80.5,0.62,0,0 198 | 15-7-2016,88,79,83.5,0,0,0 199 | 16-7-2016,90,75,82.5,T,0,0 200 | 17-7-2016,89,75,82,0,0,0 201 | 18-7-2016,93,72,82.5,0.35,0,0 202 | 19-7-2016,83,73,78,0,0,0 203 | 20-7-2016,85,68,76.5,0,0,0 204 | 21-7-2016,90,71,80.5,0,0,0 205 | 22-7-2016,94,74,84,0,0,0 206 | 23-7-2016,96,80,88,0,0,0 207 | 24-7-2016,94,75,84.5,0,0,0 208 | 25-7-2016,93,73,83,1,0,0 209 | 26-7-2016,89,72,80.5,0,0,0 210 | 27-7-2016,91,74,82.5,0,0,0 211 | 28-7-2016,95,75,85,T,0,0 212 | 29-7-2016,85,69,77,1.09,0,0 213 | 30-7-2016,84,73,78.5,0.25,0,0 214 | 31-7-2016,78,71,74.5,1.08,0,0 215 | 1-8-2016,80,69,74.5,T,0,0 216 | 2-8-2016,79,68,73.5,0,0,0 217 | 3-8-2016,80,66,73,0,0,0 218 | 4-8-2016,81,67,74,0,0,0 219 | 5-8-2016,83,69,76,0,0,0 220 | 6-8-2016,87,72,79.5,0.05,0,0 221 | 7-8-2016,86,70,78,0,0,0 222 | 8-8-2016,86,71,78.5,0,0,0 223 | 9-8-2016,87,71,79,0,0,0 224 | 10-8-2016,86,75,80.5,0.09,0,0 225 | 11-8-2016,91,74,82.5,0.15,0,0 226 | 12-8-2016,93,73,83,0.32,0,0 227 | 13-8-2016,96,81,88.5,0,0,0 228 | 14-8-2016,94,78,86,0.06,0,0 229 | 15-8-2016,92,77,84.5,0,0,0 230 | 16-8-2016,87,78,82.5,0.11,0,0 231 | 17-8-2016,85,77,81,0.01,0,0 232 | 18-8-2016,85,72,78.5,0.03,0,0 233 | 19-8-2016,88,74,81,0.01,0,0 234 | 20-8-2016,83,70,76.5,0.82,0,0 235 | 21-8-2016,86,73,79.5,0.31,0,0 236 | 22-8-2016,79,65,72,0,0,0 237 | 23-8-2016,82,61,71.5,0,0,0 238 | 24-8-2016,88,68,78,0,0,0 239 | 25-8-2016,86,69,77.5,0.01,0,0 240 | 26-8-2016,90,75,82.5,0,0,0 241 | 27-8-2016,89,73,81,0,0,0 242 | 28-8-2016,89,72,80.5,0,0,0 243 | 29-8-2016,91,74,82.5,0,0,0 244 | 30-8-2016,86,69,77.5,0,0,0 245 | 31-8-2016,89,74,81.5,T,0,0 246 | 1-9-2016,79,69,74,0.5,0,0 247 | 2-9-2016,81,66,73.5,0,0,0 248 | 3-9-2016,75,66,70.5,0,0,0 249 | 4-9-2016,80,65,72.5,0,0,0 250 | 5-9-2016,84,65,74.5,0,0,0 251 | 6-9-2016,80,71,75.5,T,0,0 252 | 7-9-2016,85,71,78,0,0,0 253 | 8-9-2016,89,71,80,0,0,0 254 | 9-9-2016,91,75,83,0.22,0,0 255 | 10-9-2016,90,74,82,T,0,0 256 | 11-9-2016,83,67,75,0,0,0 257 | 12-9-2016,78,62,70,0,0,0 258 | 13-9-2016,83,64,73.5,0,0,0 259 | 14-9-2016,91,67,79,0.56,0,0 260 | 15-9-2016,74,61,67.5,0,0,0 261 | 16-9-2016,73,61,67,0,0,0 262 | 17-9-2016,77,59,68,0,0,0 263 | 18-9-2016,82,70,76,0,0,0 264 | 19-9-2016,76,69,72.5,0.68,0,0 265 | 20-9-2016,82,68,75,0,0,0 266 | 21-9-2016,84,68,76,0,0,0 267 | 22-9-2016,86,65,75.5,0,0,0 268 | 23-9-2016,87,64,75.5,0.01,0,0 269 | 24-9-2016,71,58,64.5,0.2,0,0 270 | 25-9-2016,70,54,62,0,0,0 271 | 26-9-2016,74,54,64,0,0,0 272 | 27-9-2016,74,64,69,0.22,0,0 273 | 28-9-2016,69,56,62.5,0,0,0 274 | 29-9-2016,64,57,60.5,0,0,0 275 | 30-9-2016,59,56,57.5,0.4,0,0 276 | 1-10-2016,62,56,59,0,0,0 277 | 2-10-2016,63,57,60,0,0,0 278 | 3-10-2016,72,60,66,0,0,0 279 | 4-10-2016,69,60,64.5,0,0,0 280 | 5-10-2016,67,53,60,0,0,0 281 | 6-10-2016,73,55,64,0,0,0 282 | 7-10-2016,75,57,66,0,0,0 283 | 8-10-2016,68,58,63,0.23,0,0 284 | 9-10-2016,65,51,58,0.55,0,0 285 | 10-10-2016,64,47,55.5,0,0,0 286 | 11-10-2016,63,46,54.5,0,0,0 287 | 12-10-2016,66,53,59.5,0,0,0 288 | 13-10-2016,67,53,60,T,0,0 289 | 14-10-2016,62,47,54.5,0,0,0 290 | 15-10-2016,65,45,55,0,0,0 291 | 16-10-2016,69,53,61,0,0,0 292 | 17-10-2016,81,63,72,0,0,0 293 | 18-10-2016,81,67,74,0,0,0 294 | 19-10-2016,85,65,75,0,0,0 295 | 20-10-2016,70,62,66,0,0,0 296 | 21-10-2016,69,57,63,1.11,0,0 297 | 22-10-2016,57,47,52,0.29,0,0 298 | 23-10-2016,61,45,53,0,0,0 299 | 24-10-2016,62,47,54.5,T,0,0 300 | 25-10-2016,52,43,47.5,0,0,0 301 | 26-10-2016,51,38,44.5,0,0,0 302 | 27-10-2016,55,40,47.5,1.41,0,0 303 | 28-10-2016,51,42,46.5,0,0,0 304 | 29-10-2016,64,39,51.5,0,0,0 305 | 30-10-2016,76,54,65,0.56,0,0 306 | 31-10-2016,54,44,49,0,0,0 307 | 1-11-2016,58,40,49,0,0,0 308 | 2-11-2016,70,54,62,0,0,0 309 | 3-11-2016,72,57,64.5,0,0,0 310 | 4-11-2016,61,47,54,0,0,0 311 | 5-11-2016,62,44,53,0,0,0 312 | 6-11-2016,59,45,52,0,0,0 313 | 7-11-2016,53,41,47,0,0,0 314 | 8-11-2016,66,41,53.5,0,0,0 315 | 9-11-2016,59,49,54,0.06,0,0 316 | 10-11-2016,56,40,48,0,0,0 317 | 11-11-2016,63,41,52,0,0,0 318 | 12-11-2016,50,37,43.5,0,0,0 319 | 13-11-2016,61,41,51,0,0,0 320 | 14-11-2016,62,46,54,0,0,0 321 | 15-11-2016,56,47,51.5,1.81,0,0 322 | 16-11-2016,61,45,53,0,0,0 323 | 17-11-2016,61,49,55,0,0,0 324 | 18-11-2016,64,44,54,0,0,0 325 | 19-11-2016,63,37,50,0.25,0,0 326 | 20-11-2016,42,34,38,0.31,T,0 327 | 21-11-2016,41,36,38.5,0,0,0 328 | 22-11-2016,41,37,39,0,0,0 329 | 23-11-2016,45,35,40,0,0,0 330 | 24-11-2016,48,38,43,0.03,0,0 331 | 25-11-2016,54,45,49.5,0.02,0,0 332 | 26-11-2016,50,40,45,0,0,0 333 | 27-11-2016,50,39,44.5,0,0,0 334 | 28-11-2016,52,38,45,0,0,0 335 | 29-11-2016,60,51,55.5,2.2,0,0 336 | 30-11-2016,58,50,54,0.73,0,0 337 | 1-12-2016,54,42,48,0.07,0,0 338 | 2-12-2016,51,40,45.5,0,0,0 339 | 3-12-2016,47,41,44,T,0,0 340 | 4-12-2016,47,39,43,0,0,0 341 | 5-12-2016,49,38,43.5,0.19,0,0 342 | 6-12-2016,46,37,41.5,0.35,0,0 343 | 7-12-2016,46,40,43,0.09,0,0 344 | 8-12-2016,45,35,40,0,0,0 345 | 9-12-2016,39,29,34,0,0,0 346 | 10-12-2016,35,28,31.5,0,0,0 347 | 11-12-2016,35,28,31.5,0.03,0.4,0 348 | 12-12-2016,46,34,40,0.5,0,0 349 | 13-12-2016,43,35,39,0,0,0 350 | 14-12-2016,42,34,38,0,0,0 351 | 15-12-2016,34,19,26.5,0,T,0 352 | 16-12-2016,27,17,22,0,0,0 353 | 17-12-2016,39,24,31.5,0.73,2.8,2 354 | 18-12-2016,58,31,44.5,0.04,0,1 355 | 19-12-2016,31,23,27,0,0,0 356 | 20-12-2016,33,20,26.5,0,0,0 357 | 21-12-2016,40,30,35,0,0,0 358 | 22-12-2016,49,37,43,0,0,0 359 | 23-12-2016,47,38,42.5,0,0,0 360 | 24-12-2016,47,38,42.5,0.47,0,0 361 | 25-12-2016,50,36,43,0,0,0 362 | 26-12-2016,50,33,41.5,0.02,0,0 363 | 27-12-2016,60,40,50,0,0,0 364 | 28-12-2016,40,34,37,0,0,0 365 | 29-12-2016,46,33,39.5,0.39,0,0 366 | 30-12-2016,40,33,36.5,0.01,T,0 367 | 31-12-2016,44,31,37.5,0,0,0 368 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: bayes_course 2 | 3 | channels: 4 | - conda-forge 5 | dependencies: 6 | - python 7 | - arviz 8 | - cython 9 | - ipython 10 | - ipywidgets 11 | - jupyter_client 12 | - jupyter_core 13 | - jupyterlab 14 | - lxml 15 | - matplotlib 16 | - mkl-service 17 | - notebook 18 | - numpy 19 | - pandas 20 | - patsy 21 | - pip 22 | - pydot 23 | - python-graphviz 24 | - scikit-learn 25 | - scipy 26 | - seaborn 27 | - setuptools 28 | - xlrd 29 | - pymc3 30 | -------------------------------------------------------------------------------- /notebooks/Section3-Homework.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Section 3: Homework Exercises\n", 8 | "\n", 9 | "This material provides some hands-on experience using the methods learned from the third day's material." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [ 17 | { 18 | "name": "stderr", 19 | "output_type": "stream", 20 | "text": [ 21 | "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" 22 | ] 23 | } 24 | ], 25 | "source": [ 26 | "import matplotlib.pyplot as plt\n", 27 | "import seaborn as sns\n", 28 | "import numpy as np\n", 29 | "import pandas as pd\n", 30 | "import scipy.stats as st\n", 31 | "import pymc3 as pm\n", 32 | "import theano.tensor as tt\n", 33 | "import arviz as az" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "## Exercise: Effects of coaching on SAT scores\n", 41 | "\n", 42 | "This example was taken from Gelman *et al.* (2013):\n", 43 | "\n", 44 | "> A study was performed for the Educational Testing Service to analyze the effects of special coaching programs on test scores. Separate randomized experiments were performed to estimate the effects of coaching programs for the SAT-V (Scholastic Aptitude Test- Verbal) in each of eight high schools. The outcome variable in each study was the score on a special administration of the SAT-V, a standardized multiple choice test administered by the Educational Testing Service and used to help colleges make admissions decisions; the scores can vary between 200 and 800, with mean about 500 and standard deviation about 100. The SAT examinations are designed to be resistant to short-term efforts directed specifically toward improving performance on the test; instead they are designed to reflect knowledge acquired and abilities developed over many years of education. Nevertheless, each of the eight schools in this study considered its short-term coaching program to be successful at increasing SAT scores. Also, there was no prior reason to believe that any of the eight programs was more effective than any other or that some were more similar in effect to each other than to any other.\n", 45 | "\n", 46 | "You are given the estimated coaching effects (`d`) and their sampling variances (`s`). The estimates were obtained by independent experiments, with relatively large sample sizes (over thirty students in each school), so you can assume that they have approximately normal sampling distributions with known variances variances.\n", 47 | "\n", 48 | "Here are the data:" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 3, 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "J = 8\n", 58 | "d = np.array([28., 8., -3., 7., -1., 1., 18., 12.])\n", 59 | "s = np.array([15., 10., 16., 11., 9., 11., 10., 18.])" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "Construct an appropriate model for estimating whether coaching effects are positive, using a **centered parameterization**, and then compare the diagnostics for this model to that from an **uncentered parameterization**.\n", 67 | "\n", 68 | "Finally, perform goodness-of-fit diagnostics on the better model." 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 4, 74 | "metadata": {}, 75 | "outputs": [], 76 | "source": [ 77 | "# Write your answer here" 78 | ] 79 | } 80 | ], 81 | "metadata": { 82 | "kernelspec": { 83 | "display_name": "Python 3", 84 | "language": "python", 85 | "name": "python3" 86 | }, 87 | "language_info": { 88 | "codemirror_mode": { 89 | "name": "ipython", 90 | "version": 3 91 | }, 92 | "file_extension": ".py", 93 | "mimetype": "text/x-python", 94 | "name": "python", 95 | "nbconvert_exporter": "python", 96 | "pygments_lexer": "ipython3", 97 | "version": "3.7.6" 98 | } 99 | }, 100 | "nbformat": 4, 101 | "nbformat_minor": 4 102 | } 103 | -------------------------------------------------------------------------------- /notebooks/Section5-Homework.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Section 5: Homework Exercises\n", 8 | "\n", 9 | "This material provides some hands-on experience using the methods learned from the third day's material." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "import matplotlib.pyplot as plt\n", 19 | "import seaborn as sns\n", 20 | "import numpy as np\n", 21 | "import pandas as pd\n", 22 | "import scipy.stats as st\n", 23 | "import pymc3 as pm\n", 24 | "import theano.tensor as tt\n", 25 | "import arviz as az\n", 26 | "import io" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "## Exercise 1\n", 34 | "\n", 35 | "Consider a data set from \"Statistics: A Bayesian Perspective\", by Don Berry (1995). The dataset describes the outcome of professional golfers putting from a number of distances:" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# golf putting data from berry (1996)\n", 45 | "golf_data = \"\"\"distance tries successes\n", 46 | "2 1443 1346\n", 47 | "3 694 577\n", 48 | "4 455 337\n", 49 | "5 353 208\n", 50 | "6 272 149\n", 51 | "7 256 136\n", 52 | "8 240 111\n", 53 | "9 217 69\n", 54 | "10 200 67\n", 55 | "11 237 75\n", 56 | "12 202 52\n", 57 | "13 192 46\n", 58 | "14 174 54\n", 59 | "15 167 28\n", 60 | "16 201 27\n", 61 | "17 195 31\n", 62 | "18 191 33\n", 63 | "19 147 20\n", 64 | "20 152 24\"\"\"\n", 65 | "\n", 66 | "\n", 67 | "golf_data = pd.read_csv(io.StringIO(golf_data), sep=\" \")" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 3, 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "image/png": 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\n", 78 | "text/plain": [ 79 | "
" 80 | ] 81 | }, 82 | "metadata": { 83 | "needs_background": "light" 84 | }, 85 | "output_type": "display_data" 86 | } 87 | ], 88 | "source": [ 89 | "golf_data.assign(p=golf_data.successes/golf_data.tries).plot('distance', 'p');" 90 | ] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": {}, 95 | "source": [ 96 | "Use a latent GP to estimate the probability of success according to distance." 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 4, 102 | "metadata": {}, 103 | "outputs": [], 104 | "source": [ 105 | "# Write your answer here" 106 | ] 107 | }, 108 | { 109 | "cell_type": "markdown", 110 | "metadata": {}, 111 | "source": [ 112 | "## Exercise 2: Nashville daily temperatures\n", 113 | "\n", 114 | "The file `TNNASHVI.txt` in your data directory contains daily temperature readings for Nashville, courtesy of the [Average Daily Temperature Archive](http://academic.udayton.edu/kissock/http/Weather/). This data, as one would expect, oscillates annually. Use a Gaussian process to fit a regression model to this data." 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 5, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "daily_temps = pd.read_table(\"../data/TNNASHVI.txt\", sep='\\s+', \n", 124 | " names=['month','day','year','temp'], na_values=-99)" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 6, 130 | "metadata": {}, 131 | "outputs": [ 132 | { 133 | "data": { 134 | "text/plain": [ 135 | "" 136 | ] 137 | }, 138 | "execution_count": 6, 139 | "metadata": {}, 140 | "output_type": "execute_result" 141 | }, 142 | { 143 | "data": { 144 | "image/png": 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\n", 145 | "text/plain": [ 146 | "
" 147 | ] 148 | }, 149 | "metadata": { 150 | "needs_background": "light" 151 | }, 152 | "output_type": "display_data" 153 | } 154 | ], 155 | "source": [ 156 | "temps_2010 = daily_temps.temp[daily_temps.year>2010]\n", 157 | "temps_2010.plot(style='b.', figsize=(10,6), grid=False)" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "# Write your answer here" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "## Exercise: Random effects meta-analysis\n", 174 | "\n", 175 | "Carlin (1992) considers a Bayesian approach to meta-analysis, and includes examples of 22 trials of beta-blockers to prevent mortality after myocardial infarction. \n", 176 | "\n", 177 | "In one possible random effects model we assume the true effect (on a log-odds scale) $d_i$ in a trial $i$ is drawn from some population distribution. Let $r^C_i$ denote number of events in the control group in trial $i$, and $r^T_i$ denote events under active treatment in trial $i$. Our model is:\n", 178 | "\n", 179 | "$$\\begin{aligned}\n", 180 | "r^C_i &\\sim \\text{Binomial}\\left(p^C_i, n^C_i\\right) \\\\\n", 181 | "r^T_i &\\sim \\text{Binomial}\\left(p^T_i, n^T_i\\right) \\\\\n", 182 | "\\text{logit}\\left(p^C_i\\right) &= \\mu \\\\\n", 183 | "\\text{logit}\\left(p^T_i\\right) &= \\mu + \\delta_i \\\\\n", 184 | "\\delta_i &\\sim f\n", 185 | "\\end{aligned}$$\n", 186 | "\n", 187 | "Instead of assuming a Gaussian random effect $f$, experiment with Dirichlet process priors, and check whether it improves the resulting model." 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 7, 193 | "metadata": {}, 194 | "outputs": [], 195 | "source": [ 196 | "r_t_obs = [3, 7, 5, 102, 28, 4, 98, 60, 25, 138, 64, 45, 9, 57, 25, 33, 28, 8, 6, 32, 27, 22]\n", 197 | "n_t_obs = [38, 114, 69, 1533, 355, 59, 945, 632, 278,1916, 873, 263, 291, 858, 154, 207, 251, 151, 174, 209, 391, 680]\n", 198 | "r_c_obs = [3, 14, 11, 127, 27, 6, 152, 48, 37, 188, 52, 47, 16, 45, 31, 38, 12, 6, 3, 40, 43, 39]\n", 199 | "n_c_obs = [39, 116, 93, 1520, 365, 52, 939, 471, 282, 1921, 583, 266, 293, 883, 147, 213, 122, 154, 134, 218, 364, 674]\n", 200 | "N = len(n_c_obs)" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 8, 206 | "metadata": {}, 207 | "outputs": [], 208 | "source": [ 209 | "# Write your answer here" 210 | ] 211 | } 212 | ], 213 | "metadata": { 214 | "kernelspec": { 215 | "display_name": "bayes_course", 216 | "language": "python", 217 | "name": "bayes_course" 218 | }, 219 | "language_info": { 220 | "codemirror_mode": { 221 | "name": "ipython", 222 | "version": 3 223 | }, 224 | "file_extension": ".py", 225 | "mimetype": "text/x-python", 226 | "name": "python", 227 | "nbconvert_exporter": "python", 228 | "pygments_lexer": "ipython3", 229 | "version": "3.7.6" 230 | } 231 | }, 232 | "nbformat": 4, 233 | "nbformat_minor": 4 234 | } 235 | -------------------------------------------------------------------------------- /notebooks/Section6_1-Homework.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Rolling Regression\n", 8 | "\n", 9 | "* [Pairs trading](https://www.quantopian.com/posts/pairs-trading-algorithm-1) is a famous technique in algorithmic trading that plays two stocks against each other.\n", 10 | "* For this to work, stocks must be correlated (cointegrated).\n", 11 | "* One common example is the price of gold (GLD) and the price of gold mining operations (GFI)." 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "import pymc3 as pm\n", 21 | "import theano.tensor as tt\n", 22 | "import numpy as np\n", 23 | "import matplotlib.pyplot as plt\n", 24 | "import seaborn as sns\n", 25 | "import arviz as az\n", 26 | "import pandas as pd\n", 27 | "\n", 28 | "import warnings\n", 29 | "\n", 30 | "sampler_kwargs = {\"cores\": 4, \"chains\": 4, \"draws\": 250}\n", 31 | "\n", 32 | "warnings.simplefilter('ignore')\n", 33 | "\n", 34 | "RANDOM_SEED = 20090425" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "Lets load the prices of GFI and GLD." 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": null, 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "prices = pd.read_csv(pm.get_data('stock_prices.csv')).dropna()\n", 51 | "prices['Date'] = pd.DatetimeIndex(prices['Date'])\n", 52 | "prices = prices.set_index('Date')\n", 53 | "prices_zscored = (prices - prices.mean()) / prices.std()\n", 54 | "prices.head()" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "Plotting the prices over time suggests a strong correlation. However, the correlation seems to change over time." 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "fig = plt.figure(figsize=(9, 6))\n", 71 | "ax = fig.add_subplot(111, xlabel='Price GFI in \\$', ylabel='Price GLD in \\$')\n", 72 | "colors = np.linspace(0.1, 1, len(prices))\n", 73 | "mymap = plt.get_cmap(\"winter\")\n", 74 | "sc = ax.scatter(prices.GFI, prices.GLD, c=colors, cmap=mymap, lw=0)\n", 75 | "cb = plt.colorbar(sc)\n", 76 | "cb.ax.set_yticklabels([str(p.date()) for p in prices[::len(prices)//10].index]);" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "## Rolling regression\n", 84 | "\n", 85 | "Build a model that will allow for changes in the regression coefficients over time. Specifically, we will assume that intercept and slope follow a random-walk through time. That idea is similar to the [stochastic volatility model](stochastic_volatility.ipynb).\n", 86 | "\n", 87 | "$$ \\alpha_t \\sim \\mathcal{N}(\\alpha_{t-1}, \\sigma_\\alpha^2) $$\n", 88 | "$$ \\beta_t \\sim \\mathcal{N}(\\beta_{t-1}, \\sigma_\\beta^2) $$" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "with pm.Model() as model_randomwalk:\n", 98 | " # Build the model here" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "Inference. Despite this being quite a complex model, NUTS handles it wells." 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": null, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "with model_randomwalk:\n", 115 | " trace_rw = pm.sample(tune=2000, cores=2, \n", 116 | " target_accept=0.99)" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "### Analysis of results" 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": {}, 129 | "source": [ 130 | "As can be seen below, $\\alpha$, the intercept, changes over time." 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": null, 136 | "metadata": {}, 137 | "outputs": [], 138 | "source": [ 139 | "fig = plt.figure(figsize=(8, 6))\n", 140 | "ax = plt.subplot(111, xlabel='time', ylabel='alpha', title='Change of alpha over time.')\n", 141 | "ax.plot(trace_rw['alpha'].T, 'r', alpha=.05);\n", 142 | "ax.set_xticklabels([str(p.date()) for p in prices[::len(prices)//5].index]);" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | "As does the slope." 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": null, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "fig = plt.figure(figsize=(8, 6))\n", 159 | "ax = fig.add_subplot(111, xlabel='time', ylabel='beta', title='Change of beta over time')\n", 160 | "ax.plot(trace_rw['beta'].T, 'b', alpha=.05);\n", 161 | "ax.set_xticklabels([str(p.date()) for p in prices[::len(prices)//5].index]);" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "The posterior predictive plot shows that we capture the change in regression over time much better. Note that we should have used returns instead of prices. The model would still work the same, but the visualisations would not be quite as clear." 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": null, 174 | "metadata": {}, 175 | "outputs": [], 176 | "source": [ 177 | "fig = plt.figure(figsize=(8, 6))\n", 178 | "ax = fig.add_subplot(111, xlabel='Price GFI in \\$', ylabel='Price GLD in \\$', \n", 179 | " title='Posterior predictive regression lines')\n", 180 | "\n", 181 | "colors = np.linspace(0.1, 1, len(prices))\n", 182 | "colors_sc = np.linspace(0.1, 1, len(trace_rw[::10]['alpha'].T))\n", 183 | "mymap = plt.get_cmap('winter')\n", 184 | "mymap_sc = plt.get_cmap('winter')\n", 185 | "\n", 186 | "xi = np.linspace(prices_zscored.GFI.min(), prices_zscored.GFI.max(), 50)\n", 187 | "for i, (alpha, beta) in enumerate(zip(trace_rw[::15]['alpha'].T, \n", 188 | " trace_rw[::15]['beta'].T)):\n", 189 | " for a, b in zip(alpha[::30], beta[::30]):\n", 190 | " ax.plot(xi, a + b*xi, alpha=.01, lw=1, \n", 191 | " c=mymap_sc(colors_sc[i]))\n", 192 | " \n", 193 | "sc = ax.scatter(prices_zscored.GFI, prices_zscored.GLD, \n", 194 | " label='data', cmap=mymap, c=colors)\n", 195 | "cb = plt.colorbar(sc)\n", 196 | "cb.ax.set_yticklabels([str(p.date()) for p in prices_zscored[::len(prices)//10].index]);\n", 197 | "#ax.set(ylim=(100, 190));" 198 | ] 199 | } 200 | ], 201 | "metadata": { 202 | "anaconda-cloud": {}, 203 | "celltoolbar": "Slideshow", 204 | "kernelspec": { 205 | "display_name": "bayes_course", 206 | "language": "python", 207 | "name": "bayes_course" 208 | }, 209 | "language_info": { 210 | "codemirror_mode": { 211 | "name": "ipython", 212 | "version": 3 213 | }, 214 | "file_extension": ".py", 215 | "mimetype": "text/x-python", 216 | "name": "python", 217 | "nbconvert_exporter": "python", 218 | "pygments_lexer": "ipython3", 219 | "version": "3.7.6" 220 | }, 221 | "nbpresent": { 222 | "slides": {}, 223 | "themes": { 224 | "default": "a58a3342-e087-4f80-b6a8-4d663f20b177", 225 | "theme": { 226 | "a58a3342-e087-4f80-b6a8-4d663f20b177": { 227 | "id": "a58a3342-e087-4f80-b6a8-4d663f20b177", 228 | "palette": { 229 | "19cc588f-0593-49c9-9f4b-e4d7cc113b1c": { 230 | "id": "19cc588f-0593-49c9-9f4b-e4d7cc113b1c", 231 | "rgb": [ 232 | 252, 233 | 252, 234 | 252 235 | ] 236 | }, 237 | "31af15d2-7e15-44c5-ab5e-e04b16a89eff": { 238 | "id": "31af15d2-7e15-44c5-ab5e-e04b16a89eff", 239 | "rgb": [ 240 | 68, 241 | 68, 242 | 68 243 | ] 244 | }, 245 | "50f92c45-a630-455b-aec3-788680ec7410": { 246 | "id": "50f92c45-a630-455b-aec3-788680ec7410", 247 | "rgb": [ 248 | 155, 249 | 177, 250 | 192 251 | ] 252 | }, 253 | "c5cc3653-2ee1-402a-aba2-7caae1da4f6c": { 254 | "id": "c5cc3653-2ee1-402a-aba2-7caae1da4f6c", 255 | "rgb": [ 256 | 43, 257 | 126, 258 | 184 259 | ] 260 | }, 261 | "efa7f048-9acb-414c-8b04-a26811511a21": { 262 | "id": "efa7f048-9acb-414c-8b04-a26811511a21", 263 | "rgb": [ 264 | 25.118061674008803, 265 | 73.60176211453744, 266 | 107.4819383259912 267 | ] 268 | } 269 | }, 270 | "rules": { 271 | "blockquote": { 272 | "color": "50f92c45-a630-455b-aec3-788680ec7410" 273 | }, 274 | "code": { 275 | "font-family": "Anonymous Pro" 276 | }, 277 | "h1": { 278 | "color": "c5cc3653-2ee1-402a-aba2-7caae1da4f6c", 279 | "font-family": "Lato", 280 | "font-size": 8 281 | }, 282 | "h2": { 283 | "color": "c5cc3653-2ee1-402a-aba2-7caae1da4f6c", 284 | "font-family": "Lato", 285 | "font-size": 6 286 | }, 287 | "h3": { 288 | "color": "50f92c45-a630-455b-aec3-788680ec7410", 289 | "font-family": "Lato", 290 | "font-size": 5.5 291 | }, 292 | "h4": { 293 | "color": "c5cc3653-2ee1-402a-aba2-7caae1da4f6c", 294 | "font-family": "Lato", 295 | "font-size": 5 296 | }, 297 | "h5": { 298 | "font-family": "Lato" 299 | }, 300 | "h6": { 301 | "font-family": "Lato" 302 | }, 303 | "h7": { 304 | "font-family": "Lato" 305 | }, 306 | "pre": { 307 | "font-family": "Anonymous Pro", 308 | "font-size": 4 309 | } 310 | }, 311 | "text-base": { 312 | "font-family": "Merriweather", 313 | "font-size": 4 314 | } 315 | } 316 | } 317 | } 318 | } 319 | }, 320 | "nbformat": 4, 321 | "nbformat_minor": 1 322 | } 323 | -------------------------------------------------------------------------------- /notebooks/electricity_demand_data.py: -------------------------------------------------------------------------------- 1 | import matplotlib.dates as mdates 2 | import numpy as np 3 | 4 | # Victoria electricity demand dataset, as presented at 5 | # https://otexts.com/fpp2/scatterplots.html 6 | # and downloaded from https://github.com/robjhyndman/fpp2-package/blob/master/data/elecdaily.rda 7 | # This series contains the first eight weeks (starting Jan 1). The original 8 | # dataset was half-hourly data; here we've downsampled to hourly data by taking 9 | # every other timestep. 10 | demand_dates = np.arange('2014-01-01', '2014-02-26', dtype='datetime64[h]') 11 | demand_loc = mdates.WeekdayLocator(byweekday=mdates.WE) 12 | demand_fmt = mdates.DateFormatter('%a %b %d') 13 | 14 | demand = 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15 | temperature = 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